IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification
- Title
- IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification
- Creator
- Athithan S.; Bhola A.; Mittal S.; Sambandam R.K.; Thaiyalnayaki S.; Swapna I.
- Description
- Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy. 2022 IEEE.
- Source
- Proceedings of International Conference on Technological Advancements in Computational Sciences, ICTACS 2022, pp. 321-326.
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Random Forest; Randomized CV
- Coverage
- Athithan S., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Andhra Pradesh, Vaddeswaram, India; Bhola A., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Andhra Pradesh, Vaddeswaram, India; Mittal S., Rd Engineering College, Department of Computer Science and Engineering, U.P, Ghaziabad, India; Sambandam R.K., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Thaiyalnayaki S., Bharat Institute of Higher Education and Research, Department of Computer Science and Engineering, Chennai, India; Swapna I., Princeton Institute of Engineering and Tech for Women, Department of CSE, Telangana, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166547657-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Athithan S.; Bhola A.; Mittal S.; Sambandam R.K.; Thaiyalnayaki S.; Swapna I., “IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 21, 2025, https://archives.christuniversity.in/items/show/20191.