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Regression and Classification Approaches on Micoarray Data: A Study of Regularized Version of GLM With Application in Synthetic and Real Microarray Data - Softcover
Synopsis
In the context of microarray data, a common characteristic is that the number of parameter is greater than the number of samples (n≪p). Because of this feature, many existing methods, derived for the usual “small p and large n” problem, either cannot be applied or may not perform well. For the purpose of classification of tumor types in real and simulated microarray data using regularized and classification approaches, we have studied three regression methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), ridge regression, elastic net and four classification methods namely KNN, SVM, RDA and DLDA. In order to evaluation, we have used four readily available real microarray data sets which are Colon, Brain, SRBCT and Spira. The lasso imposes an L1 penalty and ridge regression imposes an L2 penalty; whereas, the elastic net is a balance between these two. Real data and simulation study show that the elastic net outperforms the lasso, although they both are derived from similar concept. Through the comparative study we have found that RDA performs the best for Brain, SRBCT and Spira cancer data and KNN performs better for Colon cancer data.
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About the Author
Md. Muzammel Hosen has completed his B.sc(Hon’s) and M.S(Thesis) in Statistics from Shahjalal University of Science & Technology. In order to complete his M.S, he has accomplished his work under the supervision of Professor Dr. Mohammad Shahidul Islam. At present, he is trying to pursue a M.S degree in Statistics or Bioinformatics with scholarship.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2014
- ISBN 10 3659556408
- ISBN 13 9783659556401
- BindingPaperback
- LanguageEnglish
- Number of pages112
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Regression and Classification Approaches on Micoarray Data
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LAP LAMBERT Academic Publishing Jun 2014, 2014
ISBN 10: 3659556408
ISBN 13: 9783659556401
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In the context of microarray data, a common characteristic is that the number of parameter is greater than the number of samples (n p). Because of this feature, many existing methods, derived for the usual 'small p and large n' problem, either cannot be applied or may not perform well. For the purpose of classification of tumor types in real and simulated microarray data using regularized and classification approaches, we have studied three regression methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), ridge regression, elastic net and four classification methods namely KNN, SVM, RDA and DLDA. In order to evaluation, we have used four readily available real microarray data sets which are Colon, Brain, SRBCT and Spira. The lasso imposes an L1 penalty and ridge regression imposes an L2 penalty; whereas, the elastic net is a balance between these two. Real data and simulation study show that the elastic net outperforms the lasso, although they both are derived from similar concept. Through the comparative study we have found that RDA performs the best for Brain, SRBCT and Spira cancer data and KNN performs better for Colon cancer data. 112 pp. Englisch. Seller Inventory # 9783659556401
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Regression and Classification Approaches on Micoarray Data : A Study of Regularized Version of GLM With Application in Synthetic and Real Microarray Data
Published by
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659556408
ISBN 13: 9783659556401
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In the context of microarray data, a common characteristic is that the number of parameter is greater than the number of samples (n p). Because of this feature, many existing methods, derived for the usual 'small p and large n' problem, either cannot be applied or may not perform well. For the purpose of classification of tumor types in real and simulated microarray data using regularized and classification approaches, we have studied three regression methods, namely Least Absolute Shrinkage and Selection Operator (LASSO), ridge regression, elastic net and four classification methods namely KNN, SVM, RDA and DLDA. In order to evaluation, we have used four readily available real microarray data sets which are Colon, Brain, SRBCT and Spira. The lasso imposes an L1 penalty and ridge regression imposes an L2 penalty; whereas, the elastic net is a balance between these two. Real data and simulation study show that the elastic net outperforms the lasso, although they both are derived from similar concept. Through the comparative study we have found that RDA performs the best for Brain, SRBCT and Spira cancer data and KNN performs better for Colon cancer data. Seller Inventory # 9783659556401
Quantity: 1 available
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Regression and Classification Approaches on Micoarray Data
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LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659556408
ISBN 13: 9783659556401
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