Review of Classifier Combination Methods

TitleReview of Classifier Combination Methods
Publication TypeBook Chapters
Year of Publication2008
AuthorsTulyakov S, Jaeger S, Govindaraju V, Doermann D
EditorSimone Marinai H F
Book TitleStudies in Computational Intelligence: Machine Learning in Document Analysis and RecognitionStudies in Computational Intelligence: Machine Learning in Document Analysis and Recognition
Pagination361 - 386
PublisherSpringer
Abstract

Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. By introducing different categories of classifier combinations in this review we attempt to put forward more specific directions for future theoretical research.We also introduce a retraining effect and effects of locality based training as important properties of classifier combinations. Such effects have significant influence on the performance of combinations, and their study is necessary for complete theoretical understanding of combination algorithms.