Module Combination Based on Decision Tree in Min-Max Modular Network

Abstract

The Min-Max Modular (M 3) Network is the convention solution method to large-scale and complex classification problems. We propose a new module combination strategy using a decision tree for the min-max modular network. Compared with min-max module combination method and its component classifier selection algorithms, the decision tree method has lower time complexity in prediction and better generalizing performance. Analysis of parallel subproblem learning and prediction of these different module combination methods of M 3 network show that the decision tree method is easy in parallel.