Local voting of weak classifiers
Sotiris B. Kotsiantis, Panagiotis Pintelas · International Journal of Knowledge-based and Intelligent Engineering Systems · 2005
Many data mining problems involve an investigation of relationships between features in heterogeneous datasets, where different learning algorithms can be more appropriate for different regions. We propose herein a technique of localized voting of weak classifiers. This technique identifies local r egions which have similar characteristics and then uses the votes of each local expert to describe the relationship between the data characteristics and the target class. We performed a comparison with other well known combining methods on standard benchmark datasets and the accuracy of the proposed method was greater.