Classifiers Combination Techniques: A Comprehensive Review

Abstract

In critical applications, such as medical diagnosis, security related systems, and so on, the cost or risk of action taking based on incorrect classification can be very high. Hence, combining expert opinions before taking decision can substantially increase the reliability of such systems. Such pattern recognition systems base their final decision on evidence collected from different classifiers. Such evidence can be of data type, feature type, or classifier type. Common problems in pattern recognition, such as curse of dimensionality, and small sample data size, among others, have also prompted researchers into seeking new approaches for combining evidences. This paper presents a criteria-based framework for multi-classifiers combination techniques and their areas of applications. The criteria discussed here include levels of combination, types of thresholding, adaptiveness of the combination, and ensemble-based approaches. The strengths and weaknesses of each of these categories are discussed in details. Following this analysis, we provide our perspective on the outlook of this area of research and open problems. The lack of a well-formulated theoretical framework for analyzing the performance of combination techniques is shown to provide a fertile ground for further research. In addition to summarizing the existing work, this paper also updates and complements the latest developments in this area of research.