Feature Based Architecture for Decision Fusion

Researchers continue to focus on the design of pattern recognition systems to achieve the best classification rates. Usually, different classification schemes are developed for the problem at hand, and then by experimental assessment, the best classifier is chosen as the final solution to the problem. However, it has been observed that although one design may outperform the others, the patterns that are misclassified by the different classifiers are not necessarily the same. This observation suggests that the use of multiple classifiers can enhance the decision about the patterns under classification. Also, multiple classifiers can improve the reliability of the final classifier, because the simultaneous use of different features and classification procedures. Combing the individual decisions to provide an aggregated decision is crucial to successful system design. Decision making is what separates the human species from other species. Everyday, we face a variety of choices, and we must decide which of the available actions to take. In this work, architectures and methods of aggregating decisions in a classifier ensemble environment will be investigated. The proposed architectures allow for dynamic decision fusion by utilizing the information and features of a problem via detector modules to guide the decision fusion process. The features of the problem sub-space, as well as the output, are used to identify the strength and weaknesses of the different classifiers. These features are also used to tune the aggregation procedure to better solve the problem. The main focus of this thesis is to make the final decision more dependent on the pattern being classified, and hence more dynamic. Some of the techniques that we propose to implement this dynamic fusion pertain to neural networks. Various aspects related to the design and performance of these new architectures are studied, including methods to generate the detector. An iterative training algorithm, that allows the final classification to determine whether further training should be carried out on components of these architectures, is proposed. The performance of these architectures is assessed by testing them on several benchmark problems, and the results are compared to alternative aggregation schemes. The new architectures improve the accuracy of an ensemble. Moreover, there is an improvement over existing aggregation techniques. The architectures provide a means to limit the intervention of a user while maintaining a level of accuracy that is competitive to most approaches. The time complexity of the training algorithm is shown to improve on the training requirements of the architectures.