Application and research of multi_label Naïve Bayes Classifier

Abstract

Multi_label learning and application is a new hot issue in machine learning and data mining recently. 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, authors research on classifying multi_label data based on Naïve Bayes Classifier(NBC), which is extended to multi_label learning. Training and testing procedures are adapted to the characteristics and assessment criteria of multi_label learning problem. The adapted NBC is realized through programming on MBNC experimental platform and applied to the nature scene classification, the results show that it is effective.