International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 11, No. 4, 8 Apr. 2019
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Dimensionality Reduction for Classification and Clustering
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Author(s)
Index Terms
Wrapper-based dimensionality reduction, naïve Bayes classifier, Random forest classifier, OneR classifier, Variable selection
Abstract
Now-a-days, data are generated massively from various sectors such as medical, educational, commercial, etc. Processing these data is a challenging task since the massive data take more time to process and make decision. Therefore, reducing the size of data for processing is a pressing need. The size of the data can be reduced using dimensionality reduction methods. The dimensionality reduction is known as feature selection or variable selection. The dimensionality reduction reduces the number of features present in the dataset by removing the irrelevant and redundant variables to improve the accuracy of the classification and clustering tasks. The classification and clustering techniques play a significant role in decision making. Improving accuracy of classification and clustering is an essential task of the researchers to improve the quality of decision making. Therefore, this paper presents a dimensionality reduction method with wrapper approach to improve the accuracy of classification and clustering.
Cite This Paper
D. Asir Antony Gnana Singh, E. Jebamalar Leavline, "Dimensionality Reduction for Classification and Clustering", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.4, pp.61-68, 2019. DOI:10.5815/ijisa.2019.04.06
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