Multi-label Classification with Hard-/soft-decoded Error-correcting Codes

• Main Authors: Chun-Sung Ferng, 馮俊菘
• Other Authors: Hsuan-Tien Lin
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/16421654083627886596

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === We formulate a framework for applying error-correcting codes (ECC) on multi-label classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. An immediate use of the framework is a novel ECC-based explanation of the popular random k-label-sets (RAKEL) algorithm using a simple repetition ECC. Using the framework, we empirically compare a broad spectrum of ECC designs for multi-label classification. The results not only demonstrate that RAKEL can be improved by applying some stronger ECC, but also show that the traditional Binary Relevance approach can be enhanced by learning more parity-checking labels. Our study on different ECC also helps understand the trade-off between the strength of ECC and the hardness of the base learning tasks. Furthermore, we extend our study to linear ECC for either hard (binary) or soft (real-valued) bits, and design a novel decoder for the ECC. We demonstrate that the decoder improves the performance of our framework.