Abstract:

Feature selection and parameter optimization are vital techniques in the data mining process, significantly impacting the computational costs of machine learning. Computational cost is a critical consideration in business analytics, making feature selection and parameter optimization research crucial for reducing operational costs. This study investigates the performance of 10 dimensionality reduction methods and 2 parameter optimization techniques in various business applications. The evaluation focuses on predictive accuracy and run time. The analysis reveals distinctive tendencies among the filtering methods, highlighting time-consuming behaviors in different business scenarios for Weight by Rule (WRul) and Weight by Relief (Wrel). Additionally, the study proposes a cost-effective approach to parameter optimization by utilizing grid search and evolutionary algorithms, particularly when the optimal parameter range is unknown.

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