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Sinopsis
The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.
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Reseña del editor
The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.
Biografía del autor
Dr. Sujata Dash is currently working as an Associate Professor of Computer Science Department of North Orissa University, Baripada, Odisha, India. She has 25 years of teaching and 17 years of research experience. She has published more than 85 technical papers in international journals/proceedings of international conferences/edited book.
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- EditorialLAP LAMBERT Academic Publishing
- Año de publicación2016
- ISBN 10 3659926205
- ISBN 13 9783659926204
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas192
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Knowledge Discovery using Machine Learning Algorithms
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659926205
ISBN 13: 9783659926204
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Dash SujataDr. Sujata Dash is currently working as an Associate Professor of Computer Science Department of North Orissa University, Baripada, Odisha, India. She has 25 years of teaching and 17 years of research experience. She has p. Nº de ref. del artículo: 159147775
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Knowledge Discovery using Machine Learning Algorithms
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LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 3659926205
ISBN 13: 9783659926204
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells. 192 pp. Englisch. Nº de ref. del artículo: 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
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LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659926205
ISBN 13: 9783659926204
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells. Nº de ref. del artículo: 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
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ISBN 10: 3659926205
ISBN 13: 9783659926204
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Taschenbuch. Condición: Neu. Neuware -The goal of this book is to provide a more effective way to extract features with highly important information to a specific disease, i.e. informative features, using correlation based rough set feature extraction method (RSs), rough set, genetic algorithms (GAs) and its variants, fuzzy-rough set, nearest neighbor, decision tree algorithms and partial least square method and some adaptive neural networks due to their learning abilities to construct hypotheses that can explain complex relationships in the data. This research explores the effectiveness of integrated and hybrid feature extraction methods proposed in the following chapters, in analyzing gene expression activities, based on a specific tumor disease and identifying the informative genes that underlie different precision levels in the extraction process. The identified gene subset may give an enhanced insight on the gene-gene interaction in response to different stages of abnormal cell growth which could be vital in designing treatment strategies to prevent any progression of abnormal cells.Books on Demand GmbH, Überseering 33, 22297 Hamburg 192 pp. Englisch. Nº de ref. del artículo: 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
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ISBN 10: 3659926205
ISBN 13: 9783659926204
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Paperback. Condición: Brand New. 192 pages. 8.66x5.91x0.44 inches. In Stock. Nº de ref. del artículo: 3659926205
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