多标签分类学习,This chapter reviews past and recent work on the rapidly evolving research area of multi-label data mining. Section 2 defines the two major tasks in learning from multi-label data and presents a significant number of learning methods. Sectio
Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-
This volume contains research papers accepted for presentation at the 1st International Workshop on Learning from Multi-Label Data (MLD’09), which will be held in Bled, Slovenia, at September 7, 2009 in conjunction with ECML/PKDD 2009 . MLD’09 is de
Multi-label problems arise in various domains such as multi-topic document categorization, pro- tein function prediction, and automatic image annotation. One natural way to deal with such problems is to construct a binary classifier for each label,
Most current work onclassification hasbeen focused on learningfrom a set of instances that are associated with a single label (i.e., single-label classi- fication). However, many applications, such as gene functional prediction and text categorizati
Multi-Label dimensionality Reduction Multi-label learning concerns supervised learning problems in which each instance may be associated with multiple labels simultaneously. A key difference between multi-label learning and traditional binary or mul
Multi-label text classification is a complex problem. Labels may have relationship or hierarchical structure.We investigate several neural networks for multi-label classification. Our baseline is fastText 1, a very simple model with n-gram features.
Multi-label learning studies the problem where each example is represented by a single instance while associated with a
set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging
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