Multi-label learning vector quantization algorithm

Multi-label learning is increasingly required by many domains such as text categorization and scene classification. Learning vector quantization (LVQ) offers a simple, power and scalable algorithm for the single-label learning. In this work, we adapt LVQ to solve the multi-label problems called ML-LVQ. It once adjusts two prototypes for each label of the example to minimize the ranking loss approximately for improving the ranking measures. Moreover, we arm with the single-label AdaBoost. MH as the meta-labeler to predict the number of the labels for the test examples, which will benefit the bipartitions measures. Our empirical study on 6 public multi-label benchmark datasets shows that our proposed algorithm ML-LVQ is statistically significantly better than multi-label Ad-aBoost. MH and multi-label AdaBoost with the singlelabel AdaBoost. MH as the meta-labeler especially under the evaluations of the one-error and the mac-F1 (p = 0.03).