Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

A projection method for multiobjective multiclass SVM

Support Vector Machines (SVMs) have become a very popular technique in the machine learning field for classification problems. It was originally proposed for classification of two classes. Various multiclass models with a single objective have been proposed mostly based on two families of methods: an all-together approach and a one-against-all approach. However,most of these single-objective models consider neither the different costs of misclassification nor the user's preferences. To overcome these drawbacks, multiobjective models have been proposed.In this paper we rewrite the different approaches that deal with the multiclass SVM using multiobjective techniques. These multiobjective techniques can give us weakly Pareto-optimal solutions. We propose a multiobjective technique called Projected Multiobjective All-Together(PMAT), which works in a higher-dimension space than the object space. With this technique, we can theoretically characterize the Pareto-optimal solution set. For these multiobjective techniques we get approximate sets of the Pareto-optimal solutions. For these sets, we use hypervolume and epsilon indicators to evaluate different multiobjective techniques. From the experimental results, we can see that (PMAT) outperfoms the other multiobjective techniques. When facing classification problems with very large numbers of classes, we suggest combininga tree method and multiobjective technique