Efficient Two Stage Voting Architecture for Pairwise Multi-label Classification

Abstract

A common approach for solving multi-label classification 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 classification problems with large number of labels. To tackle this problem we propose a two stage voting architecture (TSVA) for efficient pair-wise multiclass voting to the multi-label setting, which is closely related to the calibrated label ranking method. Four different real-world datasets (enron, yeast, scene and emotions) were used to evaluate the performance of the TSVA. The performance of this architecture was compared with the calibrated label ranking method with majority voting strategy and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the TSVA significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.