Classifier fusion results using various open literature data sets

In this paper, classification performance results are demonstrated for various data sets found at the University of California at Irvine's (UCI) Repository of machine learning databases. In this case, emphasis is placed on illustrating the combined effect that both feature level and classifier decision fusion has on improving overall performance for each of the data sets. Several different types of classifiers are trained using the UCI data sets. Results are shown by estimating the probability of error on independent evaluation data using cross-validation. Classifier fusion is based on majority voting and the Mean-Field BDRA. Results demonstrate that for a given data set relative performance of the various classifier types differs greatly, and that the estimated probability of error for the fused classifier, based on the Mean-Field BDRA, is lower than the best performing individual feature based classifier.