Neural Network Ensemble Model for Multi-Class Problems Based on Ensemble Learning

Decomposing multi-class problems into multiple binary-class subproblems is a common approach for multi-class classification. Traditional One-Against-All (OAA) decomposition relies more on individual classifier accuracy than diversity. This paper introduces a neural network ensemble model suitable for multi-class problems, implemented with ensemble learning techniques. The core architecture consists of a binary classifier using OAA decomposition and a complementary multi-class classifier. Validation shows this model achieves higher accuracy than classical ensemble algorithms on multi-class datasets, with advantages in reduced storage requirements and computational time. The implementation involves parallel training of base classifiers and intelligent voting mechanisms for final decision fusion.