Improved Parametric and Nonparametric Classification Techniques: The Box-Cox Transformation and Bootstrap Approach

Rahman, Md. Mahabubur; Majumder, Ajit Kumar

ISBN 10: 6202917318 / ISBN 13: 9786202917315

Published by LAP LAMBERT Academic Publishing, 2020

About this Item

Seller Inventory # I-9786202917315

Bibliographic Details

• Title: Improved Parametric and Nonparametric Classification Techniques: The Box-Cox Transformation and Bootstrap Approach
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 6202917318
• ISBN 13: 9786202917315
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This text considers different parametric and nonparametric classification techniques to classify objects, and make a comparative study among these techniques. In most of the situations, classification techniques give few misclassifications under large samples as well as under the normal populations. If the data set comes from the non-normal populations, then we apply Box-Cox transformation to transform this data set into near normal. Hence, we investigate the effect of Box-Cox transformation and see that Box-Cox transformed data generates better discrimination and classification techniques. Also if the sample size is small, then we use the Bootstrap approach for classifying objects, and investigate that the Bootstrap classification technique used in this analysis performs better than the usual techniques of small samples. There is no unique classification technique that is suitable for all the situations, also examines that nonparametric classification techniques perform better than the parametric classification techniques, whereas the Neural Network classification technique gives optimum solutions among the nonparametric classification techniques.