Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification

07/19/2022
by   Jurica Levatić, et al.
0

Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples. While SSL for the simple tasks of classification and regression has received a lot of attention from the research community, this is not properly investigated for complex prediction tasks with structurally dependent variables. This is the case of multi-label classification and hierarchical multi-label classification tasks, which may require additional information, possibly coming from the underlying distribution in the descriptive space provided by unlabeled examples, to better face the challenging task of predicting simultaneously multiple class labels. In this paper, we investigate this aspect and propose a (hierarchical) multi-label classification method based on semi-supervised learning of predictive clustering trees. We also extend the method towards ensemble learning and propose a method based on the random forest approach. Extensive experimental evaluation conducted on 23 datasets shows significant advantages of the proposed method and its extension with respect to their supervised counterparts. Moreover, the method preserves interpretability and reduces the time complexity of classical tree-based models.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/30/2022

PercentMatch: Percentile-based Dynamic Thresholding for Multi-Label Semi-Supervised Classification

While much of recent study in semi-supervised learning (SSL) has achieve...
research
08/10/2020

Feature Ranking for Semi-supervised Learning

The data made available for analysis are becoming more and more complex ...
research
07/27/2020

Oblique Predictive Clustering Trees

Predictive clustering trees (PCTs) are a well established generalization...
research
03/17/2020

The Value of Nullspace Tuning Using Partial Label Information

In semi-supervised learning, information from unlabeled examples is used...
research
06/18/2023

Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study

Electrocardiography (ECG) is a non-invasive tool for predicting cardiova...
research
05/16/2014

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms ...
research
07/05/2022

Local Multi-Label Explanations for Random Forest

Multi-label classification is a challenging task, particularly in domain...

Please sign up or login with your details

Forgot password? Click here to reset