July 2020
Intermediate to advanced
464 pages
17h 2m
English
Content preview from Mathematical Methods in Interdisciplinary Sciences
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9Fuzzy Rough Set Theory‐Based Feature Selection: A Review
Tanmoy Som1, Shivam Shreevastava2, Anoop Kumar Tiwari3, and Shivani Singh4
1Department of Mathematical Sciences, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh, 221005, India
2Department of Mathematics, SBAS, Galgotias University, Gautam Buddha Nagar, Uttar Pradesh, 201310, India
3Department of Computer Science and Engineering, Dr. K. N. Modi University, Tonk, Rajasthan, 304021, India
4DST‐Centre for Interdisciplinary Mathematical Sciences, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India
9.1 Introduction
Dimensionality reduction [1–3] has been an intriguing field of research in machine learning, signal processing, medical image processing, bioinformatics, etc. The generation of large volume of real‐valued datasets from various domains increases the requirement of dimensionality reduction in order to produce the most informative features. Dimensionality reduction likewise improves the performances of fast storage systems as well as prediction algorithms. Feature selection is one of the dimensionality reduction procedures, which preserves the striking qualities of the database systems. It shrinks exceptionally highly correlated features, which may result in lowering the overall accuracy. Attribute selection strategies center around progressively significant and non‐redundant features. It has been implemented in numerous areas such as text categorization ...
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