Machine Learning Approach

Namita Srivastava

ISBN 10: 620056843X / ISBN 13: 9786200568434

Verlag: LAP LAMBERT Academic Publishing Feb 2020, 2020

Beschreibung

This item is printed on demand - it takes 3-4 days longer - Neuware -For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers. 152 pp. Englisch.

Bestandsnummer des Verkäufers 9786200568434

Bibliografische Details

• Titel: Machine Learning Approach
• Verlag: LAP LAMBERT Academic Publishing Feb 2020
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 620056843X
• ISBN-13: 9786200568434
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

For past several years, microarray technology has attracted tremendous interest for both scientific community and industry. Recently, the applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery, etc. High dimensional data with small sample size is the main problem that generate the application of dimension reduction in microarray data analysis. It is seen that SVM, ANN and NB have recently gained wide popularity for cancer classification problems. An efficient and reliable method of dimension reduction plays an important role to improve the performance of SVM, ANN and NB, when applied for classification of high dimensional microarray data. In this book, we applied different combinations of feature selection / extraction methods, as a novel hybrid dimension reduction method for SVM, ANN and NB classifiers. The obtained results are compared with other popular published dimension reduction methods for SVM, NB and ANN classifiers.