Within supervised learning, the goal of multi-label classification is to train models to annotate objects with a subset of labels taken from a set of candidate labels. Typical applications include the determination of topics addressed in a text document, the identification of object categories present within an image, or the prediction of biological properties of a gene. In many applications, the number of candidate labels may be very large, ranging from hundreds to hundreds of thousands and often even exceeding the sample size . The very large scale nature of the output space in such problems poses both statistical and computational challenges that need to be specifically addressed.
A simple approach to solve multi-label classification problems, called binary relevance, is to train independently a binary classifier for each label. Several more complex schemes have however been proposed to take into account the dependencies between the labels (see, e.g.[28, 19, 9, 33, 10, 39]). In the context of tree-based methods, one way is to train multi-output trees [4, 18, 23], ie. trees that can predict multiple outputs at once. With respect to single-output trees 
, the score measure used in multi-output trees to choose splits is taken as the sum of the individual scores corresponding to the different labels (e.g., variance reduction) and each leaf is labeled with a vector of values, coding each for the probability of presence of one label. With respect to binary relevance, the multi-output tree approach has the advantage of building a single model for all labels. It can thus potentially take into account label dependencies and reduce memory requirements for the storage of the models. An extensive experimental comparison shows that this approach compares favorably with other approaches, including non tree-based methods, both in terms of accuracy and computing times. In addition, multi-output trees inherit all intrinsic advantages of tree-based methods, such as robustness to irrelevant features, interpretability through feature importance scores, or fast computations of predictions, that make them very attractive to address multi-label problems. The computational complexity of learning multi-output trees is however similar to that of the binary relevance method. Both approaches are indeed , where is the number of input features, the number of candidate output labels, and the sample size; this is a limiting factor when dealing with large sets of candidate labels.
One generic approach to reduce computational complexity is to apply some compression technique prior to the training stage to reduce the number of outputs to a number much smaller than the total number of labels. A model can then be trained to make predictions in the compressed output space and a prediction in the original label space can be obtained by decoding the compressed prediction. As multi-label vectors are typically very sparse, one can expect a drastic dimensionality reduction by using appropriate compression techniques. This idea has been explored for example in  using compressed sensing, and in  using bloom filters, in both cases using regularized linear models as base learners. This approach obviously reduces computing times for training the model. At the prediction stage however, the predicted compressed output needs to be decoded, which adds computational cost and can also introduce further decoding errors.
In this paper, we explore the use of random output space projections for large-scale multi-label classification in the context of tree-based ensemble methods. We first explore the idea proposed for linear models in  with random forests: a (single) random projection of the multi-label vector to an -dimensional random subspace is computed and then a multi-output random forest is grown based on score computations using the projected outputs. We exploit however the fact that the approximation provided by a tree ensemble is a weighted average of output vectors from the training sample to avoid the decoding stage: at training time all leaf labels are directly computed in the original multi-label space. We show theoretically and empirically that when is large enough, ensembles grown on such random output spaces are equivalent to ensembles grown on the original output space. When is large enough compared to , this idea hence may reduce computing times at the learning stage without affecting accuracy and computational complexity of predictions.
Next, we propose to exploit the randomization inherent to the projection of the output space as a way to obtain randomized trees in the context of ensemble methods: each tree in the ensemble is thus grown from a different randomly projected subspace of dimension . As previously, labels at leaf nodes are directly computed in the original output space to avoid the decoding step. We show, theoretically, that this idea can lead to better accuracy than the first idea and, empirically, that best results are obtained on many problems with very low values of , which leads to significant computing time reductions at the learning stage. In addition, we study the interaction between input randomization (à la Random Forests) and output randomization (through random projections), showing that there is an interest, both in terms of predictive performance and in terms of computing times, to optimally combine these two ways of randomization. All in all, the proposed approach constitutes a very attractive way to address large-scale multi-label problems with tree-based ensemble methods.
The rest of the paper is structured as follows: Section 2 reviews properties of multi-output tree ensembles and of random projections; Section 3 presents the proposed algorithms and their theoretical properties; Section 4 provides the empirical validations, whereas Section 5 discusses our work and provides further research directions.
We denote by an input space, and by an output space; without loss of generality, we suppose that (where denotes the number of input features), and that (where is the dimension of the output space). We denote by the joint (unknown) sampling density over .
Given a learning sample of observations in the form of input-output pairs, a supervised learning task is defined as searching for a function in a hypothesis space
that minimizes the expectation of some loss function
over the joint distribution of input / output pairs:
NOTATIONS: Superscript indices () denote (input, output) vectors of an observation . Subscript indices (e.g. ) denote components of vectors.
2.1 Multi-output tree ensembles
A classification or a regression tree  is built using all the input-output pairs as follows: for each node at which the subsample size is greater or equal to a pre-pruning parameter , the best split is chosen among the input features combined with the selection of an optimal cut point. The best sample split of the local subsample minimizes the average reduction of impurity
Finally, leaf statistics are obtained by aggregating the outputs of the samples reaching that leaf.
In this paper, for multi-output trees, we use the sum of the variances of the dimensions of the output vector as an impurity measure. It can be computed by (see Appendix A, in the supplementary material111 static.ajoly.org/files/ecml2014-supplementary.pdf)
Furthermore, we compute the vectors of output statistics by component-wise averaging. Notice that, when the outputs are vectors of binary class-labels (i.e.
), as in multi-label classification, the variance reduces to the so-called Gini-index, and the leaf statistics then estimate a vector of conditional probabilities, from which a prediction can be made by thresholding.
Tree-based ensemble methods build an ensemble of randomized trees. Unseen samples are then predicted by aggregating the predictions of all trees. Random Forests  build each tree on a bootstrap copy of the learning sample  and by optimising the split at each node over a locally generated random subset of size among the input features. Extra Trees  use the complete learning sample and optimize the split over a random subset of size of the features combined with a random selection of cut points. Setting the parameter to the number of input features allows to filter out irrelevant features; larger yields simpler trees possibly at the price of higher bias, and the higher the smaller the variance of the resulting predictor.
2.2 Random projections
In this paper we apply the idea of random projections to samples of vectors of the output space . With this in mind, we recall the Johnson-Lindenstrauss lemma (reduced to linear maps), while using our notations.
Johnson-Lindenstrauss lemma  Given and an integer , let be a positive integer such that . For any sample of points in there exists a matrix such that for all
Moreover, when is sufficiently large, several random matrices satisfy (4) with high probability. In particular, we can consider Gaussian matrices which elements are drawn i.i.d. in , as well as (sparse) Rademacher matrices which elements are drawn in with probability , where controls the sparsity of [1, 24].
Notice that if some satisfies (4) for the whole learning sample, it obviously satisfies (4) for any subsample that could reach a node during regression tree growing. On the other hand, since we are not concerned in this paper with the ‘reconstruction’ problem, we do not need to make any sparsity assumption ‘à la compressed sensing’.
We first present how we propose to exploit random projections to reduce the computational burden of learning single multi-output trees in very high-dimensional output spaces. Then we present and compare two ways to exploit this idea with ensembles of trees. Subsection 3.3 analyses these two ways from the bias/variance point of view.
3.1 Multi-output regression trees in randomly projected output spaces
The multi-output single tree algorithm described in section 2 requires the computation of the sum of variances in (2) at each tree node and for each candidate split. When is very high-dimensional, this computation constitutes the main computational bottleneck of the algorithm. We thus propose to approximate variance computations by using random projections of the output space. The multi-output regression tree algorithm is modified as follows (denoting by the learning sample ):
First, a projection matrix of dimension is randomly generated.
A new dataset is constructed by projecting each learning sample output using the projection matrix .
A tree (structure) is grown using the projected learning sample .
Predictions at each leaf of are computed using the corresponding outputs in the original output space.
The resulting tree is exploited in the standard way to make predictions: an input vector is propagated through the tree until it reaches a leaf from which a prediction in the original output space is directly retrieved.
If satisfies (4), the following theorem shows that variance computed in the projected subspace is an -approximation of the variance computed over the original space.
Given , a sample of points , and a projection matrix such that for all condition (4) holds, we have also:
See Appendix B, supplementary material.
As a consequence, any split score approximated from the randomly projected output space will be -close to the unprojected scores in any subsample of the complete learning sample. Thus, if condition (4) is satisfied for a sufficiently small then the tree grown from the projected data will be identical to the tree grown from the original data222Strictly speaking, this is only the case when the optimum scores of test splits as computed over the original output space are isolated, i.e. when there is only one single best split, no tie..
For a given size of the projection subspace, the complexity is reduced from to for the computation of one split score and thus from to for the construction of one full (balanced) tree, where one can expect to be much smaller than and at worst of . The whole procedure requires to generate the projection matrix and to project the training data. These two steps are respectively and but they can often be significantly accelerated by exploiting the sparsity of the projection matrix and/or of the original output data, and they are called only once before growing the tree.
All in all, this means that when is sufficiently large, the random projection approach may allow us to significantly reduce tree building complexity from to , without impact on predictive accuracy (see section 4, for empirical results).
3.2 Exploitation in the context of tree ensembles
The idea developed in the previous section can be directly exploited in the context of ensembles of randomized multi-output regression trees. Instead of building a single tree from the projected learning sample , one can grow a randomized ensemble of them. This “shared subspace” algorithm is described in pseudo-code in Algorithm 1.
Another idea is to exploit the random projections used so as to introduce a novel kind of diversity among the different trees of an ensemble. Instead of building all the trees of the ensemble from a same shared output-space projection, one could instead grow each tree in the ensemble from a different output-space projection. Algorithm 2 implements this idea in pseudo-code. The randomization introduced by the output space projection can of course be combined with any existing randomization scheme to grow ensembles of trees. In this paper, we will consider the combination of random projections with the randomizations already introduced in Random Forests and Extra Trees. The interplay between these different randomizations will be discussed theoretically in the next subsection by a bias/variance analysis and empirically in Section 4. Note that while when looking at single trees or shared ensembles, the size of the projected subspace should not be too small so that condition (4) is satisfied, the optimal value of when projections are randomized at each tree is likely to be smaller, as suggested by the bias/variance analysis in the next subsection.
From the computational point of view, the main difference between these two ways of transposing random-output projections to ensembles of trees is that in the case of Algorithm 2, the generation of the projection matrix and the computation of projected outputs is carried out times, while it is done only once for the case of Algorithm 1. These aspects will be empirically evaluated in Section 4.
3.3 Bias/variance analysis
In this subsection, we adapt the bias/variance analysis carried out in  to take into account random output projections. The details of the derivations are reported in Appendix C (supplementary material).
Let us denote by a single multi-output tree obtained from a projection matrix (below we use
to denote the corresponding random variable), whereis the value of a random variable capturing the random perturbation scheme used to build this tree (e.g., bootstrapping and/or random input space selection). The square error of this model at some point is defined by:
and its average can decomposed in its residual error, (squared) bias, and variance terms denoted:
where the variance term can be further decomposed as the sum of the following three terms:
that measure errors due to the randomness of, respectively, the learning sample, the tree algorithm, and the output space projection (Appendix C, supplementary material).
Approximations computed respectively by algorithms 1 and 2 take the following forms:
where and are vectors of i.i.d. values of the random variables and respectively.
We are interested in comparing the average errors of these two algorithms, where the average is taken over all random parameters (including the learning sample). We show (Appendix C) that these can be decomposed as follows:
From this result, it is hence clear that Algorithm 2 can not be worse, on the average, than Algorithm 1. If the additional computational burden needed to generate a different random projection for each tree is not problematic, then Algorithm 2 should always be preferred to Algorithm 1.
For a fixed level of tree randomization (), whether the additional randomization brought by random projections could be beneficial in terms of predictive performance remains an open question that will be addressed empirically in the next section. Nevertheless, with respect to an ensemble grown from the original output space, one can expect that the output-projections will always increase the bias term, since they disturb the algorithm in its objective of reducing the errors on the learning sample. For small values of , the average error will therefore decrease (with a sufficiently large number of trees) only if the increase in bias is compensated by a decrease of variance.
The value of , the dimension of the projected subspace, that will lead to the best tradeoff between bias and variance will hence depend both on the level of tree randomization and on the learning problem. The more (resp. less) tree randomization, the higher (resp. the lower) could be the optimal value of , since both randomizations affect bias and variance in the same direction.
4.1 Accuracy assessment protocol
We assess the accuracy of the predictors for multi-label classification on a test sample (TS) by the “Label Ranking Average Precision (LRAP)” , expressed by
where is the probability (or the score) associated to the label by the learnt model applied to , is a -dimensional row vector of ones, and
Test samples without any relevant labels (i.e. with ) were discarded prior to computing the average precision. The best possible average precision is thus 1. Notice that we use indifferently the notation to express the cardinality of a set or the -norm of a vector.
). The curves represent average values (and standard deviations) obtained from 10 applications of the randomised algorithms over a same singlesplit.
|Datasets||Random||Random Forests on Gaussian sub-space|
4.2 Effect of the size of the Gaussian output space
To illustrate the behaviour of our algorithms, we first focus on the “Delicious” dataset , which has a large number of labels (), of input features (), and of training () and testing () samples.
The left part of figure 1 shows, when Gaussian output-space projections are combined with the standard CART algorithm building a single tree, how the precision converges (cf Theorem 3.1) when increases towards . We observe that in this case, convergence is reached around at the expense of a slight decrease of accuracy, so that a compression factor of about 5 is possible with respect to the original output dimension .
The right part of figure 1 shows, on the same dataset, how the method behaves when combined with Random Forests. Let us first notice that the Random Forests grown on the original output space (green line) are significantly more accurate than the single trees, their accuracy being almost twice as high. We also observe that Algorithm 2 (orange curve) converges much more rapidly than Algorithm 1 (blue curve) and slightly outperforms the Random Forest grown on the original output space. It needs only about components to converge, while Algorithm 1 needs about of them. These results are in accordance with the analysis of Section 3.3, showing that Algorithm 2 can’t be inferior to Algorithm 1. In the rest of this paper we will therefore focus on Algorithm 2.
4.3 Systematic analysis over 24 datasets
To assess our methods, we have collected 24 different multi-label classification datasets from the literature (see Section D of the supplementary material, for more information and bibliographic references to these datasets) covering a broad spectrum of application domains and ranges of the output dimension (, see Table 1). For 21 of the datasets, we made experiments where the dataset is split randomly into a learning set of size , and a test set of size , and are repeated 10 times (to get average precisions and standard deviations), and for 3 of them we used a ten-fold cross-validation scheme (see Table 1).
Table 1 shows our results on the 24 multi-label datasets, by comparing Random Forests learnt on the original output space with those learnt by Algorithm 2 combined with Gaussian subspaces of size 333 is rounded to the nearest integer value; in Table 1 the values of vary between 2 for and 8 for .. In these experiments, the three parameters of Random Forests are set respectively to , (default values, see ) and (reasonable computing budget). Each model is learnt ten times on a different shuffled train/testing split, except for the 3 EUR-lex datasets where we kept the original 10 folds of cross-validation.
We observe that for all datasets (except maybe SCOP-GO), taking leads to a similar average precision to the standard Random Forests, i.e. no difference superior to one standard deviation of the error. On 11 datasets, we see that already yields a similar average precision (values not underlined in column ). For the 13 remaining datasets, increasing to significantly decreases the gap with the Random Forest baseline and 3 more datasets reach this baseline. We also observe that on several datasets such as “Drug-interaction” and “SCOP-GO”, better performance on the Gaussian subspace is attained with high output randomization () than with . We thus conclude that the optimal level of output randomization (i.e. the optimal value of the ratio ) which maximizes accuracy performances, is dataset dependent.
While our method is intended for tasks with very high dimensional output spaces, we however notice that even with relatively small numbers of labels, its accuracy remains comparable to the baseline, with suitable .
To complete the analysis, Appendix F considers the same experiments with a different base-learner (Extra Trees of ), showing very similar trends.
4.4 Input vs output space randomization
We study in this section the interaction of the additional randomization of the output space with that concerning the input space already built in the Random Forest method.
To this end, we consider the “Drug-interaction” dataset ( input features and output labels ), and we study the effect of parameter controlling the input space randomization of the Random Forest method with the randomization of the output space by Gaussian projections controlled by the parameter . To this end, Figure 2 shows the evolution of the accuracy for growing values of (i.e. decreasing strength of the input space randomization), for three different quite low values of (in this case ). We observe that Random Forests learned on a very low-dimensional Gaussian subspace (red, blue and pink curves) yield essentially better performances than Random Forests on the original output space, and also that their behaviour with respect to the parameter is quite different. On this dataset, the output-space randomisation makes the method completely immune to the ‘over-fitting’ phenomenon observed for high values of with the baseline method (green curve).
We refer the reader to a similar study on the “Delicious” dataset given in the Appendix E (supplementary material), which shows that the interaction between and may be different from one dataset to another. It is thus advisable to jointly optimize the value of and , so as to maximise the tradeoff between accuracy and computing times in a problem and algorithm specific way.
4.5 Alternative output dimension reduction techniques
In this section, we study Algorithm 2 when it is combined with alternative output-space dimensionality reduction techniques. We focus again on the “Delicious” dataset, but similar trends could be observed on other datasets.
Figure 3(a) first compares Gaussian random projections with two other dense projections: Rademacher matrices with (cf. Section 2.2) and compression matrices obtained by sub-sampling (without replacement) Hadamard matrices . We observe that Rademacher and subsample-Hadamard sub-spaces behave very similarly to Gaussian random projections.
In a second step, we compare Gaussian random projections with two (very) sparse projections: first, sparse Rademacher sub-spaces obtained by setting the sparsity parameter to and , selecting respectively about 33% and 2% of the original outputs to compute each component, and second, sub-sampled identity subspaces, similar to , where each of the selected components corresponds to a randomly chosen original label and also preserve sparsity. Sparse projections are very interesting from a computational point of view as they require much less operations to compute the projections but the number of components required for condition (4) to be satisfied is typically higher than for dense projections [24, 8]. Figure 3(b) compares these three projection methods with standard Random Forests on the “delicious” dataset. All three projection methods converge to plain Random Forests as the number of components increases but their behaviour at low values are very different. Rademacher projections converge faster with than with and interestingly, the sparsest variant () has its optimum at and improves in this case over the Random Forests baseline. Random output subspaces converge slower but they lead to a notable improvement of the score over baseline Random Forests. This suggests that although their theoretical guarantees are less good, sparse projections actually provide on this problem a better bias/variance tradeoff than dense ones when used in the context of Algorithm 2.
Another popular dimension reduction technique is the principal component analysis (PCA). In Figure3(c)
, we repeat the same experiment to compare PCA with Gaussian random projections. Concerning PCA, the curve is generated in decreasing order of eigenvalues, according to their contribution to the explanation of the output-space variance. We observe that this way of doing is far less effective than the random projection techniques studied previously.
4.6 Learning stage computing times
Our implementation of the learning algorithms is based on the scikit-learn Python package version 0.14-dev . To fix ideas about computing times, we report these obtained on a Mac Pro 4.1 with a dual Quad-Core Intel Xeon processor at 2.26 GHz, on the “Delicious” dataset. Matrix operation, such as random projections, are performed with the BLAS and the LAPACK from the Mac OS X Accelerate framework. Reported times are obtained by summing the user and sys time of the time UNIX utility.
The reported timings correspond to the following operation: (i) load the dataset in memory, (ii) execute the algorithm. All methods use the same code to build trees. In these conditions, learning a random forest on the original output space (, , ) takes 3348 s; learning the same model on a Gaussian output space of size requires 311 s, while and take respectively 236 s and 1088 s. Generating a Gaussian sub-space of size and projecting the output data of the training samples is done in less than 0.25 s, while and takes around 0.07 s and 1 s respectively. The time needed to compute the projections is thus negligible with respect to the time needed for the tree construction.
We see that a speed-up of an order of magnitude could be obtained, while at the same time preserving accuracy with respect to the baseline Random Forests method. Equivalently, for a fixed computing time budget, randomly projecting the output space allows to build more trees and thus to improve predictive performances with respect to standard Random Forests.
This paper explores the use of random output space projections combined with tree-based ensemble methods to address large-scale multi-label classification problems. We study two algorithmic variants that either build a tree-based ensemble model on a single shared random subspace or build each tree in the ensemble on a newly drawn random subspace. The second approach is shown theoretically and empirically to always outperform the first in terms of accuracy. Experiments on 24 datasets show that on most problems, using gaussian projections allows to reduce very drastically the size of the output space, and therefore computing times, without affecting accuracy. Remarkably, we also show that by adjusting jointly the level of input and output randomizations and choosing appropriately the projection method, one could also improve predictive performance over the standard Random Forests, while still improving very significantly computing times. As future work, it would be very interesting to propose efficient techniques to automatically adjust these parameters, so as to reach the best tradeoff between accuracy and computing times on a given problem.
To best of our knowledge, our work is the first to study random output projections in the context of multi-output tree-based ensemble methods. The possibility with these methods to relabel tree leaves with predictions in the original output space makes this combination very attractive. Indeed, unlike similar works with linear models [19, 10], our approach only relies on Johnson-Lindenstrauss lemma, and not on any output sparsity assumption, and also does not require to use any output reconstruction method. Besides multi-label classification, we would like to test our method on other, not necessarily sparse, multi-output prediction problems.
Arnaud Joly is research fellow of the FNRS, Belgium. This work is supported by PASCAL2 and the IUAP DYSCO, initiated by the Belgian State, Science Policy Office.
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Appendix 0.A Proof of equation (3)
The sum of the variances of observations drawn from a random vector can be interpreted as a sum of squared euclidean distances between the pairs of observations
Appendix 0.B Proof of Theorem 1
Theorem 1 Given , a sample of points and a projection matrix such that for all the Johnson-Lindenstrauss lemma holds, we have also:
Appendix 0.C Bias/variance analysis
In this subsection, we adapt the bias/variance analysis of randomised supervised learning algorithms carried out in , to assess the effect of random output projections in the context of the two algorithms studied in our paper.
0.c.1 Single random trees.
Let us denote by a single multi-output (random) tree obtained from a projection matrix (below we use to denote the corresponding random variable), where is the value of a random variable capturing the random perturbation scheme used to build this tree (e.g., bootstrapping and/or random input space selection). Denoting by the square error of this model at some point defined by:
The average of this square error can decomposed as follows:
The three terms of this decomposition are respectively the residual error, the bias, and the variance of this estimator (at ).
The variance term can be further decomposed as follows using the law of total variance:
The first term is the variance due to the learning sample randomization and the second term is the average variance (over ) due to both the random forest randomization and the random output projection. By using the law of total variance a second time, the second term of (a.13) can be further decomposed as follows:
The first term of this decomposition is the variance due to the random choice of a projection and the second term is the average variance due to the random forest randomization. Note that all these terms are non negative. In what follows, we will denote these three terms respectively , , and . We thus have:
0.c.2 Ensembles of random trees.
When the random projection is fixed for all trees in the ensemble (Algorithm 1), the algorithm computes an approximation, denoted , that takes the following form:
where a vector of i.i.d. values of the random variable . When a different random projection is chosen for each tree (Algorithm 2), the algorithm computes an approximation, denoted by , of the following form:
where is also a vector of i.i.d. random projection matrices.
We would like to compare the average errors of these two algorithms with the average errors of the original single tree method, where the average is taken for all algorithms over their random parameters (that include the learning sample).
Given that all trees are grown independently of each other, one can show that the average models corresponding to each algorithm are equal:
They thus all have the exact same bias (and residual error) and differ only in their variance.
Using the same argument, the first term of the variance decomposition in (a.13), ie. , is the same for all three algorithms since:
Their variance thus only differ in the second term in (a.13).
Again, because of the conditional independence of the ensemble terms given the and projection matrix , Algorithm 1, which keeps the output projection fixed for all trees, is such that
It thus divides the second term of (a.14) by the number of ensemble terms. Algorithm 2, on the other hand, is such that:
and thus divides the second term of (a.13) by .
Putting all these results together one gets that:
Given that all terms are positive, this result clearly shows that Algorithm 2 can not be worse than Algorithm 1.
Appendix 0.D Description of the datasets
Experiments are performed on several multi-label datasets:
the yeast 
dataset in the biology domain;
the corel5k  and
the scene 
datasets in the image domain;
the emotions  and
the CAL500 
datasets in the music domain;
the bibtex ,
the bookmarks ,
the delicious ,
the enron ,
the EUR-Lex (subject matters, directory codes and eurovoc descriptors) 
the genbase ,
the medical444 The medical dataset comes from the computational medicine
center’s 2007 medical natural language processing challenge
The medical dataset comes from the computational medicine center’s 2007 medical natural language processing challengehttp://computationalmedicine.org/challenge/previous., the tmc2007  datasets in the text domain and the mediamill  dataset in the video domain.
Several hierarchical classification tasks are also studied to increase the diversity in the number of label and treated as multi-label classification task. Each node of the hierarchy is treated as one label. Nodes of the hierarchy which never occured in the training or testing set were removed. The reuters , WIPO  datasets are from the text domain. The Diatoms  dataset is from the image domain. SCOP-GO , Yeast-GO  and Expression-GO  are from the biological domain. Missing values in the Expression-GO dataset were inferred using the median for continuous features and the most frequent value for categorical features using the entire dataset. The inference of a drug-protein interaction network  is also considered either using the drugs to infer the interactions with the protein (drug-interaction), either using the proteins to infer the interactions with the drugs (protein-interaction).
Those datasets were selected to have a wide range of number of outputs . Their basic characteristics are summarized at Table 2. For more information on a particular dataset, please see the relevant paper.
|EUR-Lex (subject matters)||19348||10-cv||5000||201|
|EUR-Lex (directory codes)||19348||10-cv||5000||412|
|EUR-Lex (eurovoc descriptors)||19348||10-cv||5000||3993|
Appendix 0.E Experiments with Extra trees
In this section, we carry out experiments combining Gaussian random projections (with ) with the Extra Trees method of  (see Section 2.1 of the paper for a very brief description of this method). Results on 23 datasets555Note that results on the “EUR-lex” dataset were not available at the time of submitting the paper. They will be added in the final version of this appendix. are compiled in Table 3.
Like for Random Forests, we observe that for all 23 datasets taking leads to a similar average precision to the standard Random Forests, ie. no difference superior to one standard deviation of the error. This is already the case with for 12 datasets and with for 4 more datasets. Interestingly, on 3 datasets with and 3 datasets with , the increased randomization brought by the projections actually improves average precision with respect to standard Random Forests (bold values in Table 3).
|Datasets||Extra trees||Extra trees on Gaussian sub-space|