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Synopsis
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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Présentation de l'éditeur
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.
Biographie de l'auteur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2016
- ISBN 10 3659926205
- ISBN 13 9783659926204
- ReliureBroché
- Langueanglais
- Nombre de pages192
- Coordonnées du fabricantnon disponible
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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
Neuf
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Etat : 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. N° de réf. du vendeur 159147775
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Knowledge Discovery using Machine Learning Algorithms
Edité par
LAP LAMBERT Academic Publishing, 2016
ISBN 10 : 3659926205
ISBN 13 : 9783659926204
Neuf
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
Edité par
LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10 : 3659926205
ISBN 13 : 9783659926204
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
Edité par
LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10 : 3659926205
ISBN 13 : 9783659926204
Neuf
Taschenbuch
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783659926204
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Knowledge Discovery using Machine Learning Algorithms
Edité par
LAP LAMBERT Academic Publishing, 2016
ISBN 10 : 3659926205
ISBN 13 : 9783659926204
Neuf
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Paperback. Etat : Brand New. 192 pages. 8.66x5.91x0.44 inches. In Stock. N° de réf. du vendeur 3659926205
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