ML-KNN: A lazy learning approach to multi-label learning

13 years 4 months ago

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Abstract: Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predeﬁned topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named Mlknn is presented, which is derived from the traditional k-Nearest Neighbor (kNN) algorithm. In detail, for each unseen instance, its k nearest neighbors in the training set are ﬁrstly identiﬁed. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three diﬀerent real-world multi-label learn...

Min-Ling Zhang, Zhi-Hua Zhou

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