Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
- Title
- Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach
- Creator
- Biju V.G.; Prashant C.M.
- Description
- In this study, Kernel Principal Component Analysis is applied to understand and visualize non-linear variation patterns by inverse mapping the projected data from a high-dimensional feature space back to the original input space. Performance Evaluation of Random Forest on various data sets has been compared to understand accuracy and various statistical measures of interest. 2016 IEEE.
- Source
- 2016 International Conference on Computer Communication and Informatics, ICCCI 2016
- Date
- 2016-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Kernel PCA; Machine Learning; Random Forest
- Coverage
- Biju V.G., Dept. of Computer Science and Engineering, Christ University Faculty of Engineering, Bangalore, India; Prashant C.M., Dept. of Computer Science and Engineering, Sapthagiri College of Engineering, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-146736679-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Biju V.G.; Prashant C.M., “Data linearity using Kernel PCA with Performance Evaluation of Random Forest for training data: A machine learning approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/20970.