Efficient multi-method rule learning for pattern classification machine learning and data mining

Abstract

The work presented here focuses on combining multiple classifiers to form single classifier for pattern classification, machine learning for expert system, and data mining tasks. The basis of the combination is that efficient concept learning is possible in many cases when the concepts learned from different approaches are combined to a more efficient concept. The experimental result of the algorithm, EMRL in a representative collection of different domain shows that it performs significantly better than the several state-of-the-art individual classifier, in case of 11 domains out of 25 data sets whereas the state-of-the-art individual classifier performs significantly better than EMRL only in 5 cases. © Springer-Verlag Berlin Heidelberg 2007.