Random feature selection using random subspace logistic regression

被引：16

作者：

Wichitaksorn, Nuttanan ^{[1
]}

Kang, Yingyue ^{[1
]}

Zhang, Faqiang ^{[1
]}

机构：

[1] Auckland Univ Technol, Dept Math Sci, Private Bag 92006, Auckland 1142, New Zealand

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2023年 / 217卷

关键词：

Feature selection; Logistic regression; Lasso logistic; Random subspace; Bootstrap; CANCER CLASSIFICATION; GENE SELECTION; INFORMATION; ALGORITHMS; PREDICTION; MODEL; SVM; GIS;

D O I：

10.1016/j.eswa.2023.119535

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we propose the random subspace logistic regression where features are randomly selected through bootstrap cycles. The random subspace regression method is applied to both standard and lasso logistic regression models. Using the simulated and empirical data, our proposed random subspace logistic regression shows favorable results and can be a promising alternative for flat feature selection.

引用

页数：8

共 78 条

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