國立虎尾科技大學 |

New theory of discriminant analysis after R. Fisher = advanced research by the feature selection method for microarray data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	New theory of discriminant analysis after R. Fisher/ by Shuichi Shinmura.
其他題名:	advanced research by the feature selection method for microarray data /
作者:	Shinmura, Shuichi.
出版者:	Singapore :Springer Singapore : : 2016.,
面頁冊數:	xx, 208 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Discriminant analysis. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-2164-0
ISBN:	9789811021640

New theory of discriminant analysis after R. Fisher = advanced research by the feature selection method for microarray data /
Shinmura, Shuichi.

New theory of discriminant analysis after R. Fisheradvanced research by the feature selection method for microarray data /[electronic resource] :by Shuichi Shinmura. - Singapore :Springer Singapore :2016. - xx, 208 p. :ill., digital ;24 cm.

This is the first book to compare eight LDFs by different types of datasets, such as Fisher's iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher's LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2) We solved the defect of the generalized inverse matrices (Problem 3) For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5) If we call the linearly separable model "Matroska," the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2) It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis.

ISBN: 9789811021640

Standard No.: 10.1007/978-981-10-2164-0doiSubjects--Topical Terms:

1116533
Discriminant analysis.

LC Class. No.: QA278.65

Dewey Class. No.: 519.535

New theory of discriminant analysis after R. Fisher = advanced research by the feature selection method for microarray data /
LDR:02456nam a2200301 a 4500 001 868729
003 DE-He213
005 20161229072550.0
006 m d
007 cr nn 008maaau
008 170720s2016 si s 0 eng d
020 $a 9789811021640 $q (electronic bk.)
020 $a 9789811021633 $q (paper)
024 7 $a 10.1007/978-981-10-2164-0 $2 doi
035 $a 978-981-10-2164-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.65
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.535 $2 23
090 $a QA278.65 $b .S556 2016
100 1 $a Shinmura, Shuichi. $3 1116532
245 1 0 $a New theory of discriminant analysis after R. Fisher $h [electronic resource] : $b advanced research by the feature selection method for microarray data / $c by Shuichi Shinmura.
260 $a Singapore : $c 2016. $b Springer Singapore : $b Imprint: Springer,
300 $a xx, 208 p. : $b ill., digital ; $c 24 cm.
520 $a This is the first book to compare eight LDFs by different types of datasets, such as Fisher's iris data, medical data with collinearities, Swiss banknote data that is a linearly separable data (LSD), student pass/fail determination using student attributes, 18 pass/fail determinations using exam scores, Japanese automobile data, and six microarray datasets (the datasets) that are LSD. We developed the 100-fold cross-validation for the small sample method (Method 1) instead of the LOO method. We proposed a simple model selection procedure to choose the best model having minimum M2 and Revised IP-OLDF based on MNM criterion was found to be better than other M2s in the above datasets. We compared two statistical LDFs and six MP-based LDFs. Those were Fisher's LDF, logistic regression, three SVMs, Revised IP-OLDF, and another two OLDFs. Only a hard-margin SVM (H-SVM) and Revised IP-OLDF could discriminate LSD theoretically (Problem 2) We solved the defect of the generalized inverse matrices (Problem 3) For more than 10 years, many researchers have struggled to analyze the microarray dataset that is LSD (Problem 5) If we call the linearly separable model "Matroska," the dataset consists of numerous smaller Matroskas in it. We develop the Matroska feature selection method (Method 2) It finds the surprising structure of the dataset that is the disjoint union of several small Matroskas. Our theory and methods reveal new facts of gene analysis.
650 0 $a Discriminant analysis. $3 1116533
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Biostatistics. $3 783654
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 670129
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-2164-0
950 $a Mathematics and Statistics (Springer-11649)