Building a Multi-Classifier System in a GA-Based Learning Environment
Yeong‐Joon Kim, Chu Leui Hong · Applied Mechanics and Materials · 2013
We have explored an approach for building a multi-classifier system in a GA-based inductive learning environment. In our approach, multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The role of a general classifier is to perform regular classification task and that of a meta-classifier is to evaluate the classification result of its general classifier and decide 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.