Integrating Global and Local Voting of Classifiers
Many data mining problems involve an investigation of the relationships between features in heterogeneous data sets, where different learning algorithms can be more appropriate for different regions. The author proposes herein a technique of integrating global and local voting of classifiers. A comparison with other well-known combining methods on standard benchmark data sets was performed, and the accuracy of the proposed method was greater.
data mining classification ensembles of classifiers machine learning