Feature ranking for multi-label classification using Markov Networks

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. Numerical experiments show that the proposed methods outperform the conventional approaches on the considered artificial and real datasets. Introduction Multi-label classification (MLC) has recently attracted a significant attention, motivated by an increasing number of applications. More examples can be found in [22], [23] and [24]. The key problem in multi-label learning is how to utilize label dependencies to improve the classification performance, motivated by which number of multi-label algorithms have been proposed in recent years (see [25] for extensive comparison of several methods). The recent progress in MLC is summarized in [26] and [22]. In MLC, each object of our interest (e.g. One of the trending challenges in MLC is a dimensionality reduction of the feature space [22], i.e. reducing the dimensionality of the vector x. Usually only some features affect y.