This paper presents a multilabel classification method that employs an error correction code together with a base ensemble learner to deal with multilabel data. It explores two different error correction codes: convolutional code and BCH code. A random forest learner is used as its based learner. The performance of the proposed method is evaluated experimentally. The popular multilabel yeast dataset is used for benchmarking. The results are compared against those of several exiting approaches. The proposed method performs well against its counterparts.
Field of Research
090609 Signal Processing 080109 Pattern Recognition and Data Mining
Socio Economic Objective
890301 Electronic Information Storage and Retrieval Services
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