Dual Layer Voting Method for Efficient Multi-label Classification

Abstract

A common approach for solving multi-label classification problems using problem-transformation methods and dichotomizing classifiers is the pairwise 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 Dual Layer Voting Method (DLVM) for efficient pair-wise multiclass voting to the multi-label setting, which is related to the calibrated label ranking method. Five different real-world datasets (enron, tmc2007, genbase, mediamill and corel5k) were used to evaluate the performance of the DLVM. The performance of this voting method was compared with the majority voting strategy used by the calibrated label ranking method and the quick weighted voting algorithm (QWeighted) for pair-wise multi-label classification. The results from the experiments suggest that the DLVM significantly outperforms the concurrent algorithms in term of testing speed while keeping comparable or offering better prediction performance.