Local Fusion with Fuzzy Integrals

Abstract

We propose a novel method for fusing different classifiers outputs. Our approach, called Context Extraction for Local Fusion with Fuzzy Integrals (CELF-FI), is a local approach that adapts fuzzy integrals fusion method to different regions of the feature space. It is based on a novel objective function that combines context identification and multi-algorithm fusion criteria into a joint objective function. This objective function is defined and optimized to produce contexts as compact clusters via unsupervised clustering. Optimization of the objective function also provide an optimal Sugeno measure within each context. Our initial experiments have indicated that the proposed fusion approach outperforms all individual classifiers, the global fuzzy integral fusion method, and the basic local fusion with linear aggregation.