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Computational Cost Reduction Strategies for Business Cases | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 16، شماره 3، مهر 2023، صفحه 757-768 اصل مقاله (837.15 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2022.337917.674929 | ||
| نویسنده | ||
| Genesis Sembiring Depari* | ||
| Faculty of Economics and Business, Universitas Pelita Harapan, Indonesia | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Evolutionary Algorithm؛ Filtering Methods؛ Grid Search؛ Parameter Optimization | ||
| مراجع | ||
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