Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification

Abstract

Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction

“…The batch method assumes that the features presented to learning are pre-available. Generally speaking, it can be further subdivided into several types according to the characteristics provided by the complex label space, including missing labels [21,22], label distribution [23,24], label selection [25], label imbalance [26,27], streaming labels [28,29], partial labels [30,31], label-specific features [32][33][34], and label correlation [35][36][37]. Among them, investigating label correlation is considered to be a favorable strategy to promote the performance of learning.…”