Title: The discovery of normality of body weight using principal component analysis: a comparative study on machine learning techniques using different data pre-processing methods

Authors: M. Sornam; M. Meharunnisa

Addresses: Department of Computer Science, University of Madras, Chennai, 600 025, TamilNadu, India ' Department of Computer Science, University of Madras, Chennai, 600 025, TamilNadu, India

Abstract: In data mining, feature selection plays an important role in finding the most important predictor variables (or features) that explain a major part of the variance of the response variable is a key to identify and build high performing models. In this proposed work, primary data is used to identify the normality/ abnormality of body weight. The missing data has been imputed by predictive mean matching (PMM) method. Efforts are made to reduce the dimensions of the data before classification using principal component analysis (PCA). The principal components obtained are passed as input to the supervised learning algorithm such as na

Keywords: missing data imputation; predictive mean matching method; pre-processing techniques; principal component analysis; PCA.

DOI: 10.1504/IJKEDM.2019.097356

International Journal of Knowledge Engineering and Data Mining, 2019 Vol.6 No.1, pp.74 - 88

Received: 06 Jun 2018
Accepted: 09 Oct 2018
Published online: 15 Jan 2019 *

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