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Feature selection with harmony search to predict baseball game result

computer-education
data-mining-and-machine-learning
data-science
optimization-theory-and-computation

Abstract

This study introduces harmony search(HS)-based feature selection to predict baseball game results using machine learning. Because baseball game data contain numerous records and are influenced by various factors, handling the features carefully when applying machine learning is essential. We employed HS for feature selection in a machine learning model to predict baseball game outcomes using Korean Baseball Organization league game data. In addition, we compare it with other feature selection methods to assess and discuss their effectiveness in selecting crucial features to predict game outcomes. The experimental results indicate that the proposed method is a competitive approach for predicting baseball game outcomes compared to traditional feature selection methods. The findings of this study are significant because they suggest that HS and metaheuristics can be applied to similar studies in the future.
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