Feature ranking for multi-label classification using Markov Networks
This work addresses feature selection for multi-label classification, which is important for domains like text categorization or bioinformatics, but it appears incremental as it builds on existing Markov Network and Ising model techniques.
The authors tackled feature ranking in multi-label classification by proposing a method based on Markov Networks, which models dependencies between labels and features to efficiently rank features and select subsets, outperforming conventional approaches on artificial and real datasets.
We propose a simple and efficient method for ranking features in multi-label classification. The method produces a ranking of features showing their relevance in predicting labels, which in turn allows to choose a final subset of features. The procedure is based on Markov Networks and allows to model the dependencies between labels and features in a direct way. In the first step we build a simple network using only labels and then we test how much adding a single feature affects the initial network. More specifically, in the first step we use the Ising model whereas the second step is based on the score statistic, which allows to test a significance of added features very quickly. The proposed approach does not require transformation of label space, gives interpretable results and allows for attractive visualization of dependency structure. We give a theoretical justification of the procedure by discussing some theoretical properties of the Ising model and the score statistic. We also discuss feature ranking procedure based on fitting Ising model using $l_1$ regularized logistic regressions. Numerical experiments show that the proposed methods outperform the conventional approaches on the considered artificial and real datasets.