An evolutionary approach to the combination of multiple classifiers to predict a stock price index

Abstract

Abstract Multiple classifier combination is a technique that combines the decisions of different classifiers. Combination can reduce the variance of estimation errors and improve the overall classification accuracy. However, direct application of combination schemes developed for pattern recognition to solving business problems has some limitations, because business problems cannot be explained completely by the results provided by machine-learning-driven classifiers alone owing to their intrinsic complexity. To solve such problems, this paper proposes an approach that is capable of incorporating the subjective problem-solving knowledge of humans into the results of quantitative models. Genetic algorithms (GAs) are used to combine classifiers stemming from machine learning, experts, and users. We use our GA-based method to predict the Korea stock price index (KOSPI).