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In this paper, a new method is introduced to construct Multiple Classifier Systems (MCSs). It is based on controlling diversity among base classifiers according to a new method in measuring diversity in kernel space. The method admits a tradeoff between individual classifier and multiple classifier accuracy and diversity as each base classifier requires knowledge of the choices made by the other MCS members. This knowledge is included in the method using data descriptors as a tool for creating diversity between base classifiers in kernel space. Data description properties are also used for measuring diversity. A new combining method presented in this paper completes this work. Performance of the proposed method is evaluated on a number of known benchmark datasets. Analyzing the results shows that the proposed method improves system's overall performance and accuracy in many cases. It also measures diversity more precisely.

In this paper, a new method in classifier fusion is introduced for decision making based on internal structure of base classifiers. Amongst methods used in combining classifiers, there are some methods which work on decision template as a tool for modeling behavior of base classifiers in order to label data. This tool models their behavior only based on their final outputs. Our new method, introduces a special structure for decision template such that internal behavior of a neural network base classifier can be modeled in a proper manner suitable for classifiers fusion. The new method builds decision template for each layer of the neural network including all hidden layers. Therefore, the process of making decision in each base classifier is also available for classifiers fusion. Efficiency of the new method is compared with some known benchmark datasets to show how it can improve efficiency of classifiers fusion.