Ensemble Selection for Cancer Diagnosis: A Novel Ensemble Selection Algorithm for Cancer Diagnosis Using Microarray Datasets - Softcover

9783659467448: Ensemble Selection for Cancer Diagnosis: A Novel Ensemble Selection Algorithm for Cancer Diagnosis Using Microarray Datasets
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Microarrays are known for their wide use in providing expression profiles for thousands of genes. Gene expression profiles provide a rich information for cancer diagnosis. Selecting an efficient classifier is a challenging task due to the presence of several classifier types. Previous studies showed that ensembles of classifiers are more efficient than single classifiers in cancer samples classification. However, designing an efficient ensemble has faced a number of challenges such as the large space of ensembles, increasing the diversity between the ensemble members, and the use of an efficient method to combine the decisions of the ensemble members. In this book, a novel ensemble selection algorithm is proposed. The proposed algorithm addresses the main challenges of the ensemble selection problem taking into consideration the special nature of microarray datasets. A set of experiments has been performed to study the robustness of ensembles of classifiers. This study shows that ensembles of classifiers are more robust than single classifiers. The study also shows that the proposed algorithm performs betten than other ensemble selection algorithms in the literature.
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Masters of Science in Bioinformatics from The Computer Science and Systems Engineering Department, Faculty of Engineering, Alexandria University & HPC System Administrator at Bibliotheca Alexanrina, Alexandria, Egypt.

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Buchbeschreibung Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Microarrays are known for their wide use in providing expression profiles for thousands of genes. Gene expression profiles provide a rich information for cancer diagnosis. Selecting an efficient classifier is a challenging task due to the presence of several classifier types. Previous studies showed that ensembles of classifiers are more efficient than single classifiers in cancer samples classification. However, designing an efficient ensemble has faced a number of challenges such as the large space of ensembles, increasing the diversity between the ensemble members, and the use of an efficient method to combine the decisions of the ensemble members. In this book, a novel ensemble selection algorithm is proposed. The proposed algorithm addresses the main challenges of the ensemble selection problem taking into consideration the special nature of microarray datasets. A set of experiments has been performed to study the robustness of ensembles of classifiers. This study shows that ensembles of classifiers are more robust than single classifiers. The study also shows that the proposed algorithm performs betten than other ensemble selection algorithms in the literature. 60 pp. Englisch. Bestandsnummer des Verkäufers 9783659467448

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Buchbeschreibung Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Microarrays are known for their wide use in providing expression profiles for thousands of genes. Gene expression profiles provide a rich information for cancer diagnosis. Selecting an efficient classifier is a challenging task due to the presence of several classifier types. Previous studies showed that ensembles of classifiers are more efficient than single classifiers in cancer samples classification. However, designing an efficient ensemble has faced a number of challenges such as the large space of ensembles, increasing the diversity between the ensemble members, and the use of an efficient method to combine the decisions of the ensemble members. In this book, a novel ensemble selection algorithm is proposed. The proposed algorithm addresses the main challenges of the ensemble selection problem taking into consideration the special nature of microarray datasets. A set of experiments has been performed to study the robustness of ensembles of classifiers. This study shows that ensembles of classifiers are more robust than single classifiers. The study also shows that the proposed algorithm performs betten than other ensemble selection algorithms in the literature. Bestandsnummer des Verkäufers 9783659467448

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Gaafar, Mohammed; Ismail, Mohamed A.; Yousri, Noha A.; Gaafar, Mohammed; Ismail, Mohamed A.; Yousri, Noha A.
ISBN 10: 3659467448 ISBN 13: 9783659467448
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Buchbeschreibung Paperback. Zustand: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock. Bestandsnummer des Verkäufers 3659467448

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Mohammed Gaafar|Mohamed A. Ismail|Noha A. Yousri
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Buchbeschreibung Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gaafar MohammedMasters of Science in Bioinformatics from The Computer Science and Systems Engineering Department, Faculty of Engineering, Alexandria University & HPC System Administrator at Bibliotheca Alexanrina, Alexandria, Egypt. Bestandsnummer des Verkäufers 5157887

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