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.
International Journal of Knowledge Engineering and Data Mining, 2019 Vol.6 No.1, pp.74 - 88
Available online: 09 Jan 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article