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Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning
Authors(4) :-Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth
Dimensionality reduction as a pre-processing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. Aim of this paper is to reduce the dimensionality of the dataset without the loss of any information from the datasets.we have implemented three dimensionality reduction algorithms.so this three algorithms are performed on two datasets, Iris and Wines datasets and the results are analyzed.
Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth
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Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 364-369
Manuscript Number : IJSRST184871
Publisher : Technoscience Academy
PRINT ISSN : 2395-6011
ONLINE ISSN : 2395-602X
Cite This Article :
Santhosh Voruganti, Karnati Ramyakrishna, Srilok Bodla, E. Umakanth, "Comparative Analysis of Dimensionality Reduction Techniques for Machine Learning", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 4, Issue 8, pp.364-369, May-June-2018.
Journal URL : http://ijsrst.com/IJSRST184871