OptiFeat: enhancing feature selection, a hybrid approach combining subject matter expertise and recursive feature elimination method

• Published in: Discover Computing Vol. 27; no. 1; p. 44
• Main Authors: Vijayakumar, G., Bharathi, R. K.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 12.11.2024; Springer Nature B.V
• Subjects: Algorithms; Automation; Big Data; Compilers; Computer Science; Data Mining and Knowledge Discovery; Data Structures and Information Theory; Efficiency; Feature selection; Information Storage and Retrieval; Machine learning; Natural Language Processing (NLP); Parameter identification; Pattern Recognition; Performance enhancement; Process parameters; Recursive functions; Virtual environments; JVM; RFE; Feature selection; GC
• ISSN: 2948-2992; 1386-4564; 2948-2992; 1573-7659
• DOI: 10.1007/s10791-024-09483-0

Optimizing the performance of Java Virtual Machines (JVMs) (Sahin et al. in Proc IEEE Int Congr Big Data BigData Congr 410–417, 2016) is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE) (Yin et al. in J Big Data 10(1):15, 2023) model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections (Khaire and Dhanalakshmi in J King Saud Univ Comput Inf Sci 34(4):1060–1073, 2022). In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance (Menéndez and Bartlett in http://arxiv.org/abs/2310.16510 , 2023). It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.