Correlation‐based classifier combination in the field of pattern recognition. (26th July 2017)

• Record Type: Journal Article
• Authors: Singh, Pawan Kumar; Sarkar, Ram; Nasipuri, Mita
• Is Part Of: Computational intelligence. Volume 34:Number 3(2018)
• Page Start: 839
• Page End: 874
• Publication Date: 2017-07-26
• Subjects: correlation‐based classifier combination -- handwritten digit recognition -- letter image recognition -- multiple correlation coefficient -- partial correlation coefficient -- rank correlation coefficient; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12135
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Classifier combination methods have proved to be an effective tool to increase the performance of classification techniques that can be used in any pattern recognition applications. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still not matured enough and achieved improvements are inconsistent. In this paper, we propose a novel statistical validation technique known as correlation‐based classifier combination technique for combining classifier in any pattern recognition problem. This validation has significant influence on the performance of combinations, and their utilization is necessary for complete theoretical understanding of combination algorithms. The analysis presented is statistical in nature but promises to lead to a class of algorithms for rank‐based decision combination. The potentials of the theoretical and practical issues in implementation are illustrated by applying it on 2 standard datasets in pattern recognition domain, namely, handwritten digit recognition and letter image recognition datasets taken from UCI Machine Learning Database Repository (http://www.ics.uci.edu/_mlearn ).1 An empirical evaluation using 8 well‐known distinct classifiers confirms the validity of our approach compared to some other combinations of multiple classifiers algorithms. Finally, we also suggest a methodology for determining the best mix of individual classifiers.