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

MATLAB 8K 185 views 0 downloads 1 credits
Tags:

Resource Overview

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.

Detailed Documentation

Decomposing multi-class problems into multiple binary-class subproblems represents a widely adopted strategy for multi-class classification tasks. Traditional One-Against-All (OAA) decomposition methods predominantly depend on individual classifier accuracy rather than exploiting classifier diversity. This paper presents a neural network ensemble model based on ensemble learning principles, specifically designed for multi-class problems. The model's foundation comprises a binary classifier implementing OAA decomposition and a supplementary multi-class classifier component. Experimental results demonstrate that this ensemble architecture achieves superior accuracy compared to classical ensemble algorithms when handling multi-class datasets, while simultaneously offering reduced storage footprint and computational overhead. The implementation leverages modular neural network design with cross-validation techniques for component optimization, incorporating confidence-based weighting in the final aggregation phase.

You May Also Like