A methodology for evaluating feature selection and clustering methods with project-specific requirements
Author:
Affiliation:
1. Fraunhofer Institute for Engineering IAO, Stuttgart, Germany
2. Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Stuttgart Germany
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2384597
Reference51 articles.
1. Approaches to Multi-Objective Feature Selection: A Systematic Literature Review
2. Unsupervised Feature Selection Based on the Distribution of Features Attributed to Imbalanced Data Sets;Alibeigi Mina;International Journal of Artificial Intelligence and Expert Systems,2011
3. Alsolai Hadeel and Marc Roper. 2019. “A Systematic Review of Feature Selection Techniques in Software Quality Prediction.” In 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA) 1–5. IEEE.
4. Alzoubi Sharaf Khalid Thaher Amayreh Mazen Mohammed Farea Yousef A. Baker El-Ebiary Syarilla Iryani Ahmad Saany and Neeraj Bisht. 2023. “A Review of Effectiveness and Efficiency Methodology of Decision Support System for Selecting Suppliers.” In 2023 International Conference on Computer Science and Emerging Technologies (CSET) 1–7. IEEE.
5. Amelio Alessia and Andrea Tagarelli. 2019. “Data Mining: Clustering.” In Encyclopedia of Bioinformatics and Computational Biology 437–448. Elsevier.
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