Attribute Selection with Genetic Algorithm - Genetic Algorithm Based Attributes Subset Selection Using Naive Bays Classifier

Feature selection is the first task of any learning approach to define a relevant set of features. Several methods are proposed to deal with the problem of feature selection including filter, wrapper and embedded methods. In this work, we focus on feature subset selection to select a minimally sized subset of optimal features. Feature Selection is optimization problem; genetic algorithm based attribute subset selection using naïve bayes classifier is used for this purpose. GABASS are found to be the best technique for selection purpose when there is very large population. The GABASS provides good results and their power lies in the good adaptation to the various and fast changing environments.

About the author

DR Somesh Kumar presently spearheads the IT department in NIET, Gr. Noida, India. He has completed his MCA in 2000, ME (CS&E) in 2006, PhD (CS) in 2011. Since 2000, he has been in teaching profession. Prof Bhupendra Kumar currently workimg in IT department in NIET,Gr. Noida,INDIA. He has been in teaching profession from 2010.

Product details

• Authors: Bhupendr Kumar, Bhupendra Kumar, Somesh Kumar
• Publisher: LAP Lambert Academic Publishing
• Languages: English
• Product format: Paperback / Softback
• Released: 31.05.2016
• EAN: 9783659871634
• ISBN: 978-3-659-87163-4
• No. of pages: 52
• Subjects: Guides; Natural sciences, medicine, IT, technology > IT, data processing