Regression and Classification Approaches on Micoarray Data: A Study of Regularized Version of GLM With Application in Synthetic and Real Microarray Data

 
9783659556401: Regression and Classification Approaches on Micoarray Data: A Study of Regularized Version of GLM With Application in Synthetic and Real Microarray Data
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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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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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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | A Study of Regularized Version of GLM With Application in Synthetic and Real Microarray Data | 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. | Format: Paperback | Language/Sprache: english | 112 pp. Seller Inventory # K9783659556401

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Book Description LAP Lambert Academic Publishing Jul 2014, 2014. Taschenbuch. Condition: Neu. 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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Book Description LAP Lambert Academic Publishing Jul 2014, 2014. Taschenbuch. Condition: Neu. 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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Book Description LAP Lambert Academic Publishing, United States, 2014. Paperback. Condition: New. Language: English . Brand New Book ***** Print on Demand *****.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 # AAV9783659556401

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Book Description LAP LAMBERT Academic Publishing. Paperback. Condition: New. 112 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.In the context of microarray data, a common characteristic is that the number of parameter is greater than the number of samples (np). 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Seller Inventory # 9783659556401

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Book Description LAP Lambert Academic Publishing Jul 2014, 2014. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand 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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