원문정보
초록
영어
Mean Field Genetic Algorithm (MGA) is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA). It combines benefit of rapid convergence property of MFA and effective genetic operations of SGA. This paper presents an approach for building a multi-classifier system in a MGA-based inductive learning environment. Multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The general classifier performs regular classification task. The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decision-making process or not. The paper discusses our approach in details and presents some empirical results that show the improvement we can achieve with our approach.
목차
1. Introduction
2. Multi-classifier System
2.1. Learning Classification Rules
2.2. Building a Multi-classifier System
3. Mean Field Genetic Algorithm
3.1. Simulated Annealing-like Genetic Algorithm (SGA)
3.2. Mean Field Annealing (MFA)
3.3. MGA Hybrid Algorithm
4. Experiments
5. Conclusions
Acknowledgements
References