Ensemble Classifier in Data Mining: Performance Enhancement of Classification using Boosting Approach on Noisy Data - Brossura

Vaghela, Vimalkumar Bhupatbhai; Vandra, Kalpesh H.; Modi, Nilesh K.

Brossura

ISBN 10: 3659114944 ISBN 13: 9783659114946

Casa editrice: LAP LAMBERT Academic Publishing, 2012

Riassunto

The generalization ability of an ensemble is usually significantly better than that of a single learner, so ensemble methods are very attractive. Authors: Dr. Kalpesh H Vandra is Director Academic and Head in CE/IT department at C. U. Shah College of Engineering, Wadhwan, Guajrat, India. Well known for his research in the areas of Data Mining and Mobile Computing. He had written more than 10 Books related to Computer & IT related area. He is Section Managing Committee Member of ISTE Gujarat Section, Life member of ISTE, member of IEEE and CSI. Dr. Nilesh K Modi is Professor & Head in Computer Science Department in Sarva Vidyalaya’s Institute of Computer Studied, S V Campus, Kadi, Gujarat, India. Well known for his research in the areas of Data Mining and Network Security. He is Associate Life Member in Computer Society of India (CSI) Mumbai, Senior Associate Member in International Association of Computer Science and Information Technology (IACSIT) Singapore, Senior Member in International Association of Engineers (IAEng) Hong Kong, Senior Member in Computer Security Institute New York.

Su questo libro

L'autore

Prof. Vimalkumar Bhupatbhai Vaghela, is currently doing Ph.D. in Computer Science & Engineering at Karpagam University, India. He is currently working as Assistant Professor in Computer Engineering Department at L. D. College of Engineering, Gujarat, India. His research areas are Relational Data Mining, Ensemble Classifier, Pattern Mining.

Dettagli dell?edizione

• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 3659114944
• ISBN 13: 9783659114946
• Rilegatura: Copertina flessibile
• Numero di pagine: 160
• Contatto del produttore: non disponibile