Combining Instance-Based Learning and Logistic Regression for Multilabel Classification
W. Cheng, und E. Hüllermeier. Machine Learning and Knowledge Discovery in Databases(2009)
Multilabel classification is an extension of conventional classification in which a single instance can be associated with
multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithmsrelying on the nearest neighbor estimation principle can be used quite successfully in this context. However, since hithertoexisting algorithms do not take correlations and interdependencies between labels into account, their potential has not yetbeen fully exploited. In this paper, we propose a new approach to multilabel classification, which is based on a frameworkthat unifies instance-based learning and logistic regression, comprising both methods as special cases. This approach allowsone to capture interdependencies between labels and, moreover, to combine model-based and similarity-based inference for multilabelclassification. As will be shown by experimental studies, our approach is able to improve predictive accuracy in terms ofseveral evaluation criteria for multilabel prediction.
