tau-FPL: Tolerance-Constrained Learning in Linear Time

01/15/2018 ∙ by Ao Zhang, et al. ∙ Georgia Institute of Technology East China Normal University 0

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed tau-FPL over existing approaches.

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1 Introduction

In real-world applications, such as spam filtering (Drucker et al., 1999) and medical diagnosing (Huang et al., 2010), the loss of misclassifying a positive instance and negative instance can be rather different. For instance, in medical diagnosing, misdiagnosing a patient as healthy is more dangerous than misclassifying healthy person as sick. Meanwhile, in reality, it is often very difficult to define an accurate cost for these two kinds of errors (Liu and Zhou, 2010; Zhou and Zhou, 2016). In such situations, it is more desirable to keep the classifier working under a small tolerance of false-positive rate (FPR) , i.e., only allow the classifier to misclassify no larger than percent of negative instances. Traditional classifiers trained by maximizing classification accuracy or AUC are not suitable due to mismatched goal.

In the literature, classification under constrained false-positive rate is known as Neyman-Pearson (NP) Classification problem (Scott and Nowak, 2005; Lehmann and Romano, 2006; Rigollet and Tong, 2011), and existing approaches can be roughly grouped into several categories. One common approach is to use cost-sensitive learning, which assigns different costs for different classes, and representatives include cost-sensitive SVM (Osuna et al., 1997; Davenport et al., 2006, 2010), cost-interval SVM (Liu and Zhou, 2010) and cost-sensitive boosting (Masnadi-Shirazi and Vasconcelos, 2007, 2011). Though effective and efficient in handling different misclassification costs, it is usually difficult to find the appropriate misclassification cost for the specific FPR tolerance. Another group of methods formulates this problem as a constrained optimization problem, which has the FPR tolerance as an explicit constraint (Mozer et al., 2002; Gasso et al., 2011; Mahdavi et al., 2013). These methods often need to find the saddle point of the Lagrange function, leading to a time-consuming alternate optimization. Moreover, a surrogate loss is often used to simplify the optimization problem, possibly making the tolerance constraint not satisfied in practice. The third line of research is scoring-thresholding methods, which train a scoring function first, then find a threshold to meet the target FPR tolerance (Drucker et al., 1999)

. In practice, the scoring function can be trained by either class conditional density estimation 

(Tong, 2013) or bipartite ranking (Narasimhan and Agarwal, 2013b). However, computing density estimation itself is another difficult problem. Also most bipartite ranking methods are less scalable with super-linear training complexity. Additionally, there are methods paying special attention to the positive class. For example, asymmetric SVM (Wu et al., 2008) maximizes the margin between negative samples and the core of positive samples, one-class SVM (Ben-Hur et al., 2001) finds the smallest ball to enclose positive samples. However, they do not incorporate the FPR tolerance into the learning procedure either.

In this paper, we address the tolerance constrained learning problem by proposing -False Positive Learning (-FPL). Specifically,

-FPL is a scoring-thresholding method. In the scoring stage, we explicitly learn a ranking function which optimizes the probability of ranking any positive instance above the centroid of the worst

percent of negative instances. Whereafter, it is shown that, with the help of our newly proposed Euclidean projection algorithm, this ranking problem can be solved in linear time under the projected gradient framework. It is worth noting that the Euclidean projection problem is a generalization of a large family of projection problems, and our proposed linear-time algorithm based on bisection and divide-and-conquer is one to three orders faster than existing state-of-the-art methods. In the thresholding stage, we devise an out-of-bootstrap thresholding method to transform aforementioned ranking function into a low false-positive classifier. This method is much less prone to overfitting compared to existing thresholding methods. Theoretical analysis and experimental results show that the proposed method achieves superior performance over existing approaches.

2 From Constrained Optimization to Ranking

In this section, we show that the FPR tolerance problem can be transformed into a ranking problem, and then formulate a convex ranking loss which is tighter than existing relaxation approaches.

Let be the instance space for some norm and be the label set, be a set of training instances, where and contains and instances independently sampled from distributions and , respectively. Let be the maximum tolerance of false-positive rate. Consider the following Neyman-Pearson classification problem, which aims at minimizing the false negative rate of classifier under the constraint of false positive rate:

(1)

where is a scoring function and is a threshold. With finite training instances, the corresponding empirical risk minimization problem is

(2)

where is the indicator function.

The empirical version of the optimization problem is difficult to handle due to the presence of non-continuous constraint; we introduce an equivalent form below.

Proposition 1.

Define be the -th largest value in multiset and be the floor function. The constrained optimization problem (2) share the same optimal solution and optimal objective value with the following ranking problem

(3)
Proof.

For a fixed , it is clear that the constraint in (2) is equivalent to . Since the objective in (2) is a non-decreasing function of , its minimum is achieved at . From this, we can transform the original problem (2) into its equivalent form (3) by substitution. ∎

Proposition 1 reveals the connection between constrained optimization (2) and ranking problem (3). Intuitively, problem (3) makes comparsion between each positve sample and the -th largest negative sample. Here we give further explanation about its form: it is equivalent to the maximization of the partial-AUC near the risk area.

Figure 1: Neyman-Pearson Classification is equivalent to a partial-AUC optimization near the specified FPR.

Although it seems that partial-AUC optimization considers fewer samples than the full AUC, optimizing (3) is intractable even if we replace by a convex loss, since the operation is non-convex when . Indeed, Theorem 1 further shows that whatever is chosen, even for some weak hypothesis of , the corresponding optimization problem is NP-hard.

Definition 1.

A surrogate function of   is a contionuous and non-increasing function satisfies that (i) , ; (ii) , ; (iii) as .

Theorem 1.

For fixed , and surrogate function , optimization problem -

with hyperplane hypothesis set

is NP-hard.

This intractability motivates us to consider the following upper bound approximation of (3):

(4)

which prefers scores on positive examples to exceed the mean of scores on the worst -proportion of negative examples. If , optimization (4) is equivalent to the original problem (3). In general cases, equality also could hold when both the scores of the worst -proportion negative examples are the same.

The advantages of considering ranking problem (4) include (details given later):

  • For appropriate hypothesis set of , replacing by its convex surrogate will produce a tight convex upper bound of the original minimization problem (3), in contrast to the cost-sensitive classification which may only offer an insecure lower bound. This upper bound is also tighter than the convex relaxation of the original constrained minimization problem (2) (see Proposition 2);

  • By designing efficient learning algorithm, we can find the global optimal solution of this ranking problem in linear time, which makes it well suited for large-scale scenarios, and outperforms most of the traditional ranking algorithms;

  • Explicitly takes into consideration and no additional cost hyper-parameters required;

  • The generalization performance of the optimal solution can be theoretically established.

2.1 Comparison to Alternative Methods

Before introducing our algorithm, we briefly review some related approaches to FPR constrained learning, and show that our approach can be seen as a tighter upper bound, meanwhile maintain linear training complexity.

Cost sensitive Learning One alternative approach to eliminating the constraint in (2) is to approximate it by introducing asymmetric costs for different types of error into classification learning framework:

where is a hyper-parameter that punishes the gain of false positive instance. Although reasonable for handling different misclassification costs, we point out that methods under this framework indeed minimize a lower bound of problem (2). This can be verified by formulating (2) into unconstrained form

Thus, for a fixed , minimizing is equivalent to minimize a lower bound of . In other words, cost-sensitive learning methods are insecure in this setting: mismatched can easily violate the constraint on FPR or make excessive requirements, and the optimal for specified is only known after solving the original constrained problem, which can be proved NP-hard.

In practice, one also has to make a continuous approximation for the constraint in (2) for tractable cost-sensitive training, this multi-level approximation makes the relationship between the new problem and original unclear, and blocks any straightforward theoretical justification.

Constrained Optimization  The main difficulty of employing Lagrangian based method to solve problem (2) lies in the fact that both the constraint and objective are non-continuous. In order to make the problem tractable while satisfying FPR constraint strictly, the standard solution approach relies on replacing with its convex surrogate function (see Definition 1) to obtain a convex constrained optimization problem 111For precisely approximating the indicator function in constraint, choosing “bounded” loss such as sigmoid or ramp loss (Collobert et al., 2006; Gasso et al., 2011) etc. seems appealing. However, bounded functions always bring about a non-convex property, and corresponding problems are usually NP-hard (Yu et al., 2012). These difficulties limit both efficient training and effective theoretical guarantee of the learned model.. Interestingly, Proposition 2 shows that whichever surrogate function and hypothesis set are chosen, the resulting constrained optimization problem is a weaker upper bound than that of our approach.

Proposition 2.

For any non-empty hypothesis set and , convex surrogate function , let

we have

Thus, in general, there is an exact gap between our risk and convex constrained optimization risk . In practice one may prefer a tighter approximation since it represents the original objective better. Moreover, our Theorem 5 also achieves a tighter bound on the generalization error rate, by considering empirical risk .

Ranking-Thresholding Traditional bipartite ranking methods usually have super-linear training complexity. Compared with them, the main advantage of our algorithm comes from its linear-time conplexity in each iteration without any convergence rate (please refer to Table 1). We named our algorithm -FPL, and give a detailed description of how it works in the next sections.

3 Tolerance-Constrained False Positive Learning

Based on previous discussion, our method will be divided into two stages, namely scoring and thresholding. In scoring, a function is learnt to maximize the probability of giving higher a score to positive instances than the centroid of top percent of the negative instances. In thresholding, a suitable threshold will be chosen, and the final prediction of an instance can be obtained by

(5)

3.1 Tolerance-Constrained Ranking

In (4), we consider linear scoring function , where

is the weight vector to be learned,

regularization and replace by some of its convex surrogate . Kernel methods can be used for nonlinear ranking functions. As a result, the learning problem is

(6)

where is the regularization parameter, and .

Directly minimizing (6) can be challenging due to the operator, we address it by considering its dual,

Theorem 2.

Define and be the matrix containing positive and negative instances in their rows respectively, the dual problem of (6) can be written by

(7)

where and are dual variables, is the convex conjugate of , and the domain is defined as

Let and be the optimal solution of (7), the optimal

(8)

According to Theorem 1, learning scoring function is equivalent to learning the dual variables and by solving problem (7). Its optimization naturally falls into the area of projected gradient method. Here we choose where due to its simplicity of conjugation. The key steps are summarized in Algorithm 1. At each iteration, we first update solution by the gradient of the objective function , then project the dual solution onto the feasible set . In the sequel, we will show that this projection problem can be efficiently solved in linear time. In practice, since is smooth, we also leverage Nesterov’s method to further accelerate the convergence of our algorithm. Nesterov’s method (Nesterov, 2003) achieves convergence rate for smooth objective function, where is the number of iterations.

0:  , maximal FPR tolerance , regularization parameter , stopping condition
1:  Randomly initialize and
2:  Set counter:
3:  while  or  do
4:     Compute gradient of at point
5:     Compute by gradient descent;
6:     Project onto the feasible set :
7:     Update counter: ;
8:  end while
9:  Return
Algorithm 1 -FPL Ranking

3.2 Linear Time Projection onto the Top-k Simplex

One of our main technical results is a linear time projection algorithm onto , even in the case of is close to . For clear notations, we reformulate the projection problem as

(9)

It should be noted that, many Euclidean projection problems studied in the literature can be seen as a special case of this problem. If the term is fixed, or replaced by a constant upper bound , we obtain a well studied case of continuous quadratic knapsack problem (CQKP)

where . Several efficient methods based on median-selecting or variable fixing techniques are available (Patriksson, 2008). On the other hand, if , all upper bounded constraints are automatically satisfied and can be omitted. Such special case has been studied, for example, in (Liu and Ye, 2009) and (Li et al., 2014), both of which achieve complexity.

Unfortunately, none of those above methods can be directly applied to solving the generalized case (9), due to its property of unfixed upper-bound constraint on when . To our knowledge, the only attempt to address the problem of unfixed upper bound is (Lapin et al., 2015). They solve a similar (but simpler) problem

based on sorting and exhaustive search and their method achieves a runtime complexity , which is super-linear and even quadratic when and are linearly dependent. By contrast, our proposed method can be applied to both of the aforementioned special cases with minor changes and remains complexity. The notable characteristic of our method is the efficient combination of bisection and divide-and-conquer: the former offers the guarantee of worst complexity, and the latter significantly reduces the large constant factor of bisection method.

We first introduce the following theorem, which gives a detailed description of the solution for (9).

Theorem 3.

is the optimal solution of (9) if and only if there exist dual variables , , satisfy the following system of linear constraints:

(10)
(11)
(12)

and , .

Based on Theorem 3, the projection problem can be solved by finding the value of three dual variables , and that satisfy the above linear system. Here we first propose a basic bisection method which guarantees the worst time complexity. Similar method has also been used in (Liu and Ye, 2009). For brevity, we denote and the -largest dimension in and respectively, and define function , , and as follows222Indeed, for some , is not one-valued and thus need more precise definition. Here we omit it for brevity, and leave details in section 6.6.:

(13)
(14)
(15)
(16)

The main idea of leveraging bisection to solve the system in theorem 3 is to find the root of . In order to make bisection work, we need three conditions: should be continuous; the root of can be efficiently bracketed in a interval; and the value of at the two endpoints of this interval have opposite signs. Fortunately, based on the following three lemmas, all of those requirements can be satisfied.

Lemma 1.

(Zero case)   is an optimal solution of (9) if and only if .

Lemma 2.

(Bracketing ) If , .

Lemma 3.

(Monotonicity and convexity)

  1. is convex, continuous and strictly decreasing in , ;

  2. is continuous, monotonically decreasing in ;

  3. is continuous, strictly increasing in ;

  4. is continuous, strictly increasing in .

Furthermore, we can define the inverse function of as , and rewrite as:

(17)

it is a convex function of , strictly decreasing in .

Lemma 1 deals with the special case of . Lemma 2 and 3 jointly ensure that bisection works when ; Lemma 2 bounds ; Lemma 3 shows that is continuous, and since it is also strictly increasing, the value of at two endpoints must has opposite sign.

Basic method: bisection & leverage convexity We start from select current in the range . Then compute corresponding by (13) in , and use the current to compute by (14). Computing can be completed in by a well-designed median-selecting algorithm (Kiwiel, 2007). With the current (i.e. updated) , and in hand, we can evaluate the sign of in and determine the new bound of . In addition, the special case of can be checked using Lemma 1 in by a linear-time k-largest element selecting algorithm (Kiwiel, 2005). Since the bound of is irrelevant to and , the number of iteration for finding is , where is the maximum tolerance of the error. Thus, the worst runtime of this algorithm is . Furthermore, we also leverage the convexity of and to further improve this algorithm, please refer to (Liu and Ye, 2009) for more details about related techniques.

Although bisection solves the projections in linear time, it may lead to a slow convergence rate. We further improve the runtime complexity by reducing the constant factor . This technique benefits from exploiting the monotonicity of both functions , , and , which have been stated in Lemma 3. Notice that, our method can also be used for finding the root of arbitary piecewise linear and monotone function, without the requirement of convexity.

0:  , maximal accuracy
1:  Calculate initial uncertainly intervals for , , and ;
2:  Initialize breakpoint caches for , , , :
3:  Initialize partial sums of , , , with zero;
4:  Set and ;
5:  while  or  do
6:     Calculate , , , (by leveraging corresponding caches and partial sums);
7:     Prune caches and update partial sums;
8:     Shrink intervals of , , and based on sign of ;
9:     ;
10:     Set as midpoint of current new interval
11:  end while
12:  Return , ,
Algorithm 2 Linear-time Projection on Top-k simplex

Improved method: endpoints Divide & Conquer Lemma 3 reveals an important chain monotonicity between the dual variables, which can used to improve the performance of our baseline method. The key steps are summarized in Algorithm 2. Denote the value of a variable in iteration as . For instance, if , from emma 3 we have , and . This implies that we can set uncertainty intervals for both , , and . As the interval of shrinking, lengths of these four intervals can be reduced simultaneously. On the other hand, notice that is indeed piecewise linear function (at most segments), the computation of its value only contains a comparison between and all of the s. By keeping a cache of s and discard those elements which are out of the current bound of in advance, in each iteration we can reduce the expected comparison counts by half. A more complex but similar procedure can also be applied for computing , , and , because both of these functions are piecewise linear and the main cost is the comparison with endpoints. As a result, for approximately linear function and evenly distributed breakpoints, if the first iteration of bisection costs time, the overall runtime of the projection algorithm will be , which is much less than the original bisection algorithm whose runtime is .

3.3 Convergence and Computational Complexity

Following immediately from the convergence result of Nesterov’s method, we have:

Theorem 4.

Let and be the output from the -FPL algorithm after T iterations, then , where .

Algorithm Training Validation
-FPL Linear
TopPush Linear
CS-SVM Quadratic
Linear
Bipartite Linear
Ranking
Table 1: Complexity comparison with SOTA approaches

Finally, the computational cost of each iteration is dominated by the gradient evaluation and the projection step. Since the complexity of projection step is and the cost of computing the gradient is , combining with Theorem 4 we have that: to find an -suboptimal solution, the total computational complexity of -FPL is . Table 1 compares the computational complexity of -FPL with that of some state-of-the-art methods. The order of validation complexity corresponds to the number of hyper-parameters. From this, it is easy to see that -FPL is asymptotically more efficient.

3.4 Out-of-Bootstrap Thresholding

In the thresholding stage, the task is to identify the boundary between the positive instances and percent of the negative instances. Though thresholding on the training set is commonly used in (Joachims, 1996; Davenport et al., 2010; Scheirer et al., 2013), it may result in overfitting. Hence, we propose an out-of-bootstrap method to find a more accurate and stable threshold. At each time, we randomly split the training set into two sets and , and then train on as well as the select threshold on

. The procedure can be running multiple rounds to make use of all the training data. Once the process is completed, we can obtain the final threshold by averaging. On the other hand, the final scoring function can be obtained by two ways: learn a scoring function using the full set of training data, or gather the weights learned in each previous round and average them. This method combines both the advantages of out-of-bootstrap and soft-thresholding techniques: accurate error estimation and reduced variance with little sacrifice on the bias, thus fits the setting of thresholding near the risk area.

4 Theoretical Guarantees

Now we develop the theoretical guarantee for the scoring function, which bounds the probability of giving any positive instances higher score than proportion of negative instances. To this end, we first define , the probability for any negative instance to be ranked above using , i.e. , and then measure the quality of by , which is the probability of giving any positive instances lower score than percent of negative instances. The following theorem bounds by the empirical loss .

Theorem 5.

Given training data consisting of independent instances from distribution and independent instances from distribution , let be the optimal solution to the problem (6). Assume and . We have, for proper and any , with a probability at least ,

(18)

where , and .

Theorem 5 implies that if is upper bounded by , the probability of ranking any positive samples below percent of negative samples is also bounded by . If is approaching infinity, would be close to 0, which means in that case, we can almost ensure that by thresholding at a suitable point, the true-positive rate will get close to 1. Moreover, we observe that and play different roles in this bound. For instance, it is well known that the largest absolute value of Gaussian random instances grows in . Thus we believe that the growth of only slightly affects both the largest and the centroid of top-proportion scores of negatives samples. This leads to a conclusion that increasing only slightly raise , but significant reduce the margin between target and . On the other hand, increasing will reduce upper bound of , thus increasing the chance of finding positive instances at the top. In sum, and control and respectively.

5 Experiment Results

5.1 Effectiveness of the Linear-time Projection

We first demonstrate the effectiveness of our projection algorithm. Following the settings of (Liu and Ye, 2009)

, we randomly sample 1000 samples from the normal distribution

and solve the projection problem. The comparing method is ibis (Liu and Ye, 2009), an improved bisection algorithm which also makes use of the convexity and monotonicity. All experiments are running on an Intel Core i5 Processor. As shown in Fig.2, thanks to the efficient reduction of the constant factor, our method outperforms ibis by saving almost of the running time in the limit case.

We also solve the projection problem proposed in (Lapin et al., 2015) by using a simplified version of our method, and compare it with the method presented in (Lapin et al., 2015) (PTkC), whose complexity is . As one can observe from Fig.2(b), our method is linear in complexity regarding with and does not suffer from the growth of . In the limit case (both large and ), it is more than three-order of magnitude faster than the competitors.

(a) Run time against the method ibis (log-log).
(b) Run time against PTkC (log-log).
Figure 2: Running time against two peer projection methods.

5.2 Ranking Performance

Dataset heart spambase real-sim w8a
120/150,d:13 1813/2788,d:57 22238/50071,d:20958 2933/62767,d:300
5 10 0.1 0.5 1 5 10 0.01 0.1 1 5 10 0.05 0.1 0.5 1 5 10
CS-SVM .526 .691 .109 .302 .811 .920 .376 .921 .972 .990 .501 .520 .649 .695 .828 .885
TopPush .711 .112 .303 .484 .774 .845 .747 .920 .968 .983 .627 .656 .761 .842
.509 .728 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
-Rank
2-Rank
Table 2: Ranking performance by different values of the tolerance . The number of positive/negative instances and feature dimensions (‘d’) is shown together with the name of each dataset. The best results are shown in bold. ’N/A’s denote the experiments that require more than one week for training.

Next, we validate the ranking performance of our -FPL method, i.e. scoring and sorting test samples, and then evaluate the proportion of positive samples ranked above proportion of negative samples. Considering ranking performance independently can avoid the practical problem of mismatching the constraint in (2) on testing set, and always offer us the optimal threshold.

Specifically, we choose (3) as evaluation and validation criterion. Compared methods include cost-sensitive SVM (CS-SVM) (Osuna et al., 1997), which has been shown a lower bound approximation of (3); TopPush (Li et al., 2014) ranking, which focus on optimizing the absolute top of the ranking list, also a special case of our model (); (Narasimhan and Agarwal, 2013a), a more general method which designed for optimizing arbitrary partial-AUC. We test two version of our algorithms: -Rank and -Rank, which correspond to the different choice of in learning scheme. Intuitively, enlarge in training phase can be seen as a top-down approximation—from upper bound to the original objective (2). On the other hand, the reason for choosing is that, roughly speaking, the average score of the top proportion of negative samples may close to the score of -th largest negative sample.

Settings. We evaluate the performance on publicly benchmark datasets with different domains and various sizes333https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary. For small scale datasets( instances), 30 times stratified hold-out tests are carried out, with data as train set and data as test set. For large datasets, we instead run 10 rounds. In each round, hyper-parameters are chosen by 5-fold cross validation from grid, and the search scope is extended if the optimal is at the boundary.

Results. Table 2 reports the experimental results. We note that at most cases, our proposed method outperforms other peer methods. It confirms the theoretical analysis that our methods can extract the capacity of the model better. For TopPush, it is highly-competitive in the case of extremely small , but gradually lose its advantage as increase. The algorithm of is based on cutting-plane methods with exponential number of constraints, similar technologies are also used in many other ranking or structured prediction methods, e.g. Structured SVM (Tsochantaridis et al., 2005). The time complexity of this kind of methods is , and we found that even for thousands of training samples, it is hard to finish experiments in allowed time.

5.3 Overall Classification Accuracy

Dataset(+/-) (%) BS-SVM CS-LR CS-SVM CS-SVM-OOB -FPL -FPL
heart 5
120/150,d:13 10
breast-cancer 1
239/444 5
d:10 10
spambase 0.5
1813/2788 1
d:57 5
10
real-sim 0.01
22238/50071 0.1
d:20958 0.5
1
5
10
w8a 0.05
1933/62767 0.1
d:123 0.5
1
5
10
Table 3: [(mean false positive rate, mean true positive rate), NP-score] on real-world datasets by different values of the tolerance . In the leftmost column, the number of positive/negative instances and feature dimensions (‘d’) in each dataset. For each dataset, the best results are shown in bold.

In this section we compare the performance of different models by jointly learning the scoring function and threshold in training phase, i.e. output a classifier. To evaluate a classifier under the maximum tolerance, we use Neyman-Pearson score (NP-score) (Scott, 2007). The NP-score is defined by where and are false-positive rate and true-positive rate of the classifier respectively, and is the maximum tolerance. This measure punishes classifiers whose false-positive rates exceed , and the punishment becomes higher as .

Settings

. We use the similar setting for classification as for ranking experiments, i.e., for small scale datasets, 30 times stratified hold-out tests are carried out; for large datasets, we instead run 10 rounds. Comparison baselines include: Cost-Sensitive Logistic Regression (CS-LR) which choose a surrogate function that different from CS-SVM; Bias-Shifting Support Vector Machine (BS-SVM), which first training a standard SVM and then tuning threshold to meet specified false-positive rate; cost-sensitive SVM (CS-SVM). For complete comparison, we also construct a CS-SVM by our out-of-bootstrap thresholding (CS-SVM-OOB), to eliminate possible performance gains comes from different thresholding method, and focus on the training algorithm itself. For all of comparing methods, the hyper-parameters are selected by 5-fold cross-validation with grid search, aims at minimizing the NP-score, and the search scope is extended when the optimal value is at the boundary. For our

-FPL, in the ranking stage the regularization parameter is selected to minimize (3) , and then the threshold is chosen to minimize NP-score. We test two variants of our algorithms: -FPL and -FPL, which corresponding different choice of in learning scheme. As mentioned previously, enlarge can be seen as a top-down approximation towards the original objective.

Results. The NP-score results are given in Table 3. First, we note that both our methods can achieve the best performance in most of the tests, compared to various comparing methods. Moreover, it is clear that even using the same method to select the threshold, the performance of cost sensitive method is still limited. Another observation is that both of the three algorithms which using out-of-bootstrap thresholding can efficiently control the false positive rate under the constraint. Moreover, -FPLs are more stable than other algorithms, which we believe benefits from the accurate splitting of the positive-negative instances and stable thresholding techniques.

5.4 Scalability

Figure 3: Training time of -FPL versus training data size for different (log-log).

We study how -FPL scales to a different number of training examples by using the largest dataset real-sim. In order to simulate the limit situation, we construct six datasets with different data size, by up-sampling original dataset. The sampling ratio is , thus results in six datasets with data size from 72309 to 2313888. We running -FPL ranking algorithm on these datasets with different and optimal (chosen by cross-validation), and report corresponding training time. Up-sampling technology ensures that, for a fixed , all the six datasets share the same optimal regularization parameter . Thus the unique variable can be fixed as data size. Figure 3 shows the log-log plot for the training time of -FPL versus the size of training data, where different lines correspond to different . It is clear that the training time of -FPL is indeed linear dependent in the number of training data. This is consistent with our theoretical analysis and also demonstrate the scalability of -FPL.

6 Proofs and Technical Details

In this section, we give all the detailed proofs missing from the main text, along with ancillary remarks and comments.

Notation. In the following, we define be the -th largest dimension of a vector , define ,