Aggregation of neural classifiers using Choquet integral with respect to a fuzzy measure

Data classification appears in many real-world problems, e.g., recognition of image patterns, differentiation among species of plants, classifying between benign and malignant tumors, among others. Many of these problems present data patterns, which are difficult to be identified, thus requiring more advanced techniques to be solved. Over the last few years, various classification algorithms have been developed to address these problems, but there is no classifier able to be the best choice in all situations. As a simple and effective methodology, an ensemble of classifiers has been applied to several classification problems aiming to improve performance and increase reliability. However, for an ensemble of classifiers to be able to improve the classification accuracy, an aggregation technique must be performed. In this work, we present an aggregation methodology for an ensemble of neural classifiers using Choquet integral with respect to a fuzzy measure based on Shannon's entropy. We apply this methodology to conventional benchmarks and large databases and the results are promising.