A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification

Abstract

For any pattern classification task, an increase in data size, number of classes, dimension of the feature space, and interclass separability affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem domain. Most modern techniques of pattern classification use a combination of classifiers and fuse the decisions provided by the same, often using only a selected set of appropriate features for the task. The problem of selection of a useful set of features and discarding the ones which do not provide class separability are addressed in feature selection and fusion tasks. This paper presents a review of the different techniques and algorithms used in decision fusion and feature fusion strategies, for the task of pattern classification. A survey of the prominent techniques used for decision fusion, feature selection, and fusion techniques has been discussed separately. The different techniques used for fusion have been categorized based on the applicability and methodology adopted for classification. A novel framework has been proposed by us, combining both the concepts of decision fusion and feature fusion to increase the performance of classification. Experiments have been done on three benchmark datasets to prove the robustness of combining feature fusion and decision fusion techniques.