A New Multiple Classifiers Combination Algorithm

Classification has an important role in data mining, but the individual classifier has its limited applicable field, so combining the classified output of multiple classifiers to get much more accuracy is very valuable. There are many combination algorithms such as product, sum, median and vote rules. But these integration algorithms always have not good capability in different datasets. So in this paper a new parallel multiple classifiers combining algorithm, that is maximum of posterior probability average with self-adaptive weight based on output vectors and decision template (MASWOD) is proposed. The experiment on standard UCI dataset show that this algorithm improve the classified accuracy and extend the applicable area of data mining greatly