Combining classifiers with multimethod approach

摘要

The automatic induction of classifiers from examples is an important technique used in data mining. One of the problems encountered is how to induce a good classifier without overfitting. Although there is a lot of research going on in this field, the research is mainly focused on a specific machine learning method or on a specific combination of those methods. In this paper a multimethod approach to combine classifiers is presented that combines advantages of single methods and avoids theirs disadvantages at the same time by applying different methods on the same knowledge model, each of which may contain inherent limitations, with the expectation that the combined multiple methods may produce better results.