Multilabel Classification by BCH Code and Random Forests
Abbas Z. Kouzani, Gulisong Nasireding · 2009
Abstract—Multilabel classification deals with problems in which an instance can belong to multiple classes. This paper uses error correcting codes for multilabel classification. BCH code and Random Forests learner are used to form the proposed method. Thus, the advantage of the error-correcting properties of BCH is merged with the good performance of the random forests learner to enhance the multilabel classification results. Three experiments are conducted on three common benchmark datasets. The results are compared against those of several exiting approaches. The proposed method does well against its counterparts for the three datasets of varying characteristics. Index Terms—multilabel data, multilabel classification, error correcting codes, BCH code, ensemble learners, random forests I.