Classifier fusion framework using genetic algorithms

In this work a hierarchical fusion framework for melding multiple classifiers has been introduced, to obtain improved performance for classification problems. The fusion framework is hybrid in nature which allows for feature and decision level fusion while also being application and data agnostic. With a set of data features and a pool of trainable classifiers as input, the fusion framework utilizes a Genetic Algorithm (GA) with a modified chromosome structure to identify the appropriate choice of classifiers, select feature inputs for each classifier, and to determine the suitable hierarchical structure for a three layered hybrid classifier fusion scheme. The paper describes the workings of the framework and shows results of improved performance over individual classifiers and the majority voting scheme when applied to physiological condition classification.