Data Mining-Based Variant Subset Features
- Title
- Data Mining-Based Variant Subset Features
- Creator
- Chakraborty S.; Islam S.H.; Samanta D.
- Description
- A subset of accessible variants data is chosen for the learning approaches during the variant selection procedure. Itincludes the important one with the fewest dimensions and contributes the most to learner accuracy. The benefit of variant selection would be that essential information about a particular variant isnt lost, but if just a limited number of variants are needed,and the original variants are extremely varied, there tends to be a risk of information being lost since certain variants must be ignored. Dimensional reduction, also based on variant extraction, on the other hand, allows the size of the variant space to be reduced without losing information from the original variant space.Filters, wrappers, and entrenched approaches are the three categories of variant selection procedures. Wrapper strategies outperform filter methods because the variation selection procedure is suited for the classifier to be used. Wrapper techniques, on the other hand, are too expensive to use for large variant spaces due to their high computational cost;therefore each variant set must be evaluated using the trained classifier, which slows down the variant selection process. Filter techniques have a lower computing cost and are faster than wrapper procedures, but they have worse classification reliability and are better suited to high-dimensional datasets. Hybrid techniques, which combine the benefits of both filters and wrappers approaches, are now being organized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Source
- EAI/Springer Innovations in Communication and Computing, pp. 177-193.
- Date
- 2022-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Coverage
- Chakraborty S., JIS University, Dum Dum Cantonment, India; Islam S.H., Indian Institute of Information Technology Kalyani, West Bengal, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 25228595
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Chakraborty S.; Islam S.H.; Samanta D., “Data Mining-Based Variant Subset Features,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18675.