Two Stage Classifier Chain Architecture For Efficient Pair-Wise Multi-Label Learning

Abstract

A common approach for solving multi-label learning problems using problem-transformation methods and dichotomizing classifiers is the pair-wise decomposition strategy. One of the problems with this approach is the need for querying a quadratic number of binary classifiers for making a prediction that can be quite time consuming, especially in learning problems with large number of labels. To tackle this problem we propose a Two Stage Classifier Chain Architecture (TSCCA) for efficient pair-wise multi-label learning. Six different real-world datasets were used to evaluate the performance of the TSCCA. The performance of the architecture was compared with six methods for multi-label learning and the results suggest that the TSCCA outperforms the concurrent algorithms in terms of predictive accuracy. In terms of testing speed TSCCA shows better performance comparing to the pair-wise methods for multi-label learning.