Error-Correcting Output Codes for Multi-Label Text Categorization

When a sample belongs to more than one label from a set of available classes, the classification problem (known as multi-label classification) turns to be more complicated. Text data, widely available nowadays in the world wide web, is an obvious instance example of such a task. This paper presents a new method for multi-label text categorization created by modifying the Error-Correcting Output Coding (ECOC) technique. Using a set of binary complimentary classifiers, ECOC has proven to be efficient for multi-class problems. The proposed method, called ML-ECOC, is a first attempt to extend the ECOC algorithm to handle multi-label tasks. Experimental results on the Reuters benchmarks (RCV1-v2) demonstrate the potential of the proposed method on multi-label text categorization.