Enhanced Logistic Regression (ELR) Model for Big Data

Dhamodharavadhani S Dhamodharavadhani S,Rathipriya R Rathipriya R

doi:10.4018/978-1-7998-0106-1.ch008

Enhanced Logistic Regression (ELR) Model for Big Data

Dhamodharavadhani S Dhamodharavadhani S, Rathipriya R Rathipriya R

https://doi.org/10.4018/978-1-7998-0106-1.ch008

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Publication Date: Oct 31, 2019

Citations: 11

Affiliation: Periyar University

#Logistic Regression Model #Regression Model + Show 8 more

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Abstract

Regression model is an important tool for modeling and analyzing data. In this chapter, the proposed model comprises three phases. The first phase concentrates on sampling techniques to get best sample for building the regression model. The second phase is to predict the residual of logistic regression (LR) model using time series analysis method: autoregressive. The third phase is to develop enhanced logistic regression (ELR) model by combining both LR model and residual prediction (RP) model. The empirical study is carried out to study the performance of the ELR model using large diabetic dataset. The results show that ELR model has a higher level of accuracy than the traditional logistic regression model.

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