New Theory of Discriminant Analysis After R. Fisher : Advanced Research by the Feature Selection Method for Microarray Data. 1st ed. 2016

種類:

電子ブック

責任表示:

by Shuichi Shinmura

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2016

著者名:

• Shinmura, Shuichi.
• SpringerLink (Online service)

ISBN:

9789811021640 [9811021643]    

注記:

1 New Theory of Discriminant Analysis -- 1.1 Introduction -- 1.2 Motivation for our Research -- 1.3 Discriminant Functions -- 1.4 Unresolved Problem (Problem 1) -- 1.5 LSD Discrimination (Problem 2) -- 1.6 Generalized Inverse Matrices (Problem 3) -- 1.7 K-fold Cross-validation (Problem 4) -- 1.8 Matroska Feature Selection Method (Problem 5) -- 1.9 Summary -- References -- 2 Iris Data and Fisher’s Assumption -- 2.1 Introduction -- 2.2 Iris Data -- 2.3 Comparison of Seven LDFs -- 2.4 100-folf Cross-validation for Small Sample Method (Method 1) -- 2.5 Summary -- References -- 3 The Cephalo-Pelvic Disproportion (CPD) Data with Collinearity -- 3.1 Introduction -- 3.2 CPD Data -- 3.3 100-folf Cross-validation -- 3.4 Trial to Remove Collinearity -- 3.5 Summary -- References -- 4 Student Data and Problem 1 -- 4.1 Introduction -- 4.2 Student Data -- 4.3 100-folf Cross-validation for Student Data -- 4.4 Student Linearly Separable Data -- 4.5 Summary -- References -- 5 The Pass/Fail Determination using Exam Scores -A T
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

ローカル注記:

学内専用E-BOOKS (local access only)