Exploring label correlations using decision templates for ensemble of classifier chains

Abstract

The use of ensemble-based multi-label methods has been shown to be effective in improving multi-label classification results. One of the most widely used ensemble-based multi-label classifiers is Ensemble of Classifier Chains. Decision templates for Ensemble of Classifier Chains (DTECC) is a fusion scheme based on Decision Templates that combines the predictions of Ensemble of Classifier Chains using information from the decision profile for each label, without considering information about other labels that might contribute to the classified result. Based on DTECC, this work proposes the Unconditionally Dependent Decision Templates for Ensemble of Classifier Chains (UDDTECC) method, a classifier fusion method that seeks to exploit correlations between labels in the fusion process. In this way, the classification of each label in the problem takes into account the label values that are considered conditionally dependent and that can lead to an improvement in the classification performance. The proposed method is experimentally compared with two traditional classifier fusion strategies and with a stacking-based strategy. Empirical evidence shows that using the proposed Decision Templates adaptation can improve the performance compared to the traditionally used fusion schemes on most of the evaluated metrics.

Figures (8)

• Figure 1: The Stacking Classifier Ensemble training diagram.
• Figure 2: Schematic representation of the Stacking Classifier Ensemble's classification process for an unseen instance.
• Figure 3: Matrix of the $\phi$ coefficients absolute values of the Scene dataset labels.
• Figure 4: Example of classifier fusion using the UDDTECC method on an instance of the Scene data set for the label Mountain.
• Figure 5: UDDTECC training workflow.