Multi-label learning via minimax probability machine

Abstract

In this paper, we propose Minimax Probability Machine for Multi-label data classification and is termed as Multi-Label Minimax Probability Machine (MLMPM). Based on data mean and covariance information, MLMPM builds a classifier that minimizes an upper bound on the mis-classification probability of unseen future data. For capturing label correlation we have considered asymmetric co-occurrency matrix into the model. The proposed model has also been extended to non-linear settings using the Mercer Kernel trick. To accelerate the training procedure, iterative weighted least squares is used to train the underlying optimization model efficiently. Extensive experimental comparisons of our proposed method with related multi-label algorithms on synthetic as well as real world multi-label datasets, along with Amazon rainforest satellite images dataset, prove its efficacy.