Abstract
Since standard data sets are not capable enough in evaluating classifier combination methods in multiple classifier systems, a new classifier simulator with sufficient diversity is proposed to generate artificial data sets. The simulator can generate simulating data for a problem of any number of classes and any classifier performance, and can also show pair wise dependency. It is achieved via a three-step algorithm: firstly building the confusion matrices of the classifiers on the basis of desired behavior, secondly generating the outputs of one classifier based on its confusion matrix, and then producing the outputs of other classifiers. The detailed generating algorithm is discussed. Experiments on majority voting combination method shows that negative correlation could improve the accuracy of multiple classifier systems, which indicates the validity of the proposed simulator.
This work is supported by the Major State Basic Research Development Program of China (973 Program), No.2004CB719401.
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Yang, Ly., Qin, Z. (2005). Design of a New Classifier Simulator. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_26
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DOI: https://doi.org/10.1007/11494683_26
Publisher Name: Springer, Berlin, Heidelberg
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