Prediction of Customer Attrition using Feature Extraction Techniques and its Performance Assessment through dissimilar Classifiers
R Suguna, M Shyamala Devi, P Praveen Kumar… - Advances in Decision …, 2020 - Springer
Advances in Decision Sciences, Image Processing, Security and Computer Vision …, 2020•Springer
Dimensionality reduction is the process of identifying insignificant data variables and
dropping them. The process culminates in obtaining a set of principal variables.
Dimensionality reduction not only removes the redundant features, also reduces storage
and computation time. Feature Selection and Feature extraction are the two components in
dimensionality reduction. This paper explores techniques used for feature extraction and
analyzes the results by applying the techniques to customer churn dataset. The performance …
dropping them. The process culminates in obtaining a set of principal variables.
Dimensionality reduction not only removes the redundant features, also reduces storage
and computation time. Feature Selection and Feature extraction are the two components in
dimensionality reduction. This paper explores techniques used for feature extraction and
analyzes the results by applying the techniques to customer churn dataset. The performance …
Abstract
Dimensionality reduction is the process of identifying insignificant data variables and dropping them. The process culminates in obtaining a set of principal variables. Dimensionality reduction not only removes the redundant features, also reduces storage and computation time. Feature Selection and Feature extraction are the two components in dimensionality reduction. This paper explores techniques used for feature extraction and analyzes the results by applying the techniques to customer churn dataset. The performance of these techniques in different classifiers is also compared and results are visualized in graphs.
Springer
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