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.
Highlights
In machine learning applications, the traditional single label classification (SLC) problem has been explored substantially
A new construction method of chain mechanism that only considers the coupled labels and inserts them into the chain simultaneously, and improves the prediction performance; A novel feature selection function that is integrated into the partial classifier chain method with feature selection (PCC-FS) method by exploiting the coupling relationships between features and labels, reducing the number of redundant features and enhancing the classification performance; Label couplings extracted from the multi-label classification (MLC) problem based on the theory of coupling learning, including intra-couplings within labels and inter-couplings between features and labels, which makes the exploration of label correlation more comprehensive
Eight state-of-the-art MLC algorithms were chosen for a comparison study in order to act as a contrast to the proposed probabilistic classifier chains (PCC)-FS algorithm
Summary
The traditional single label classification (SLC) problem has been explored substantially. We propose a partial classifier chain method with feature selection (PCC-FS) that exploits the coupling relationships in the MLC problem. A novel feature selection function that is integrated into the PCC-FS method by exploiting the coupling relationships between features and labels, reducing the number of redundant features and enhancing the classification performance; Label couplings extracted from the MLC problem based on the theory of coupling learning, including intra-couplings within labels and inter-couplings between features and labels, which makes the exploration of label correlation more comprehensive. This consists of three components: feature selection with inter-coupling exploration, intra-coupling exploration in label set, and label set prediction.
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