Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression

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Zarei Shaho; MOHAMMADPOUR ADEL

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Journal Paper

Paper Information

Journal: Computational Methods for Differential Equations Year:2019 | Volume:7 | Issue:4 (Special Issue) Page(s): 626-634

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Title

Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression

Author(s)

Zarei Shaho | MOHAMMADPOUR ADEL | Issue Writer Certificate

Keywords

High-dimensional classification

Logistic regression classifir

Dimensionality reduction

Ran-dom forest

Finite population Bayesian bootstrapping

Abstract

Traditional logistic regression is plugged with degenerates and violent behavior in High-dimensional classification, because of the problem of non-invertible matrices in estimating model parameters. In this paper, to overcome the high-dimensionality of data, we introduce two new algorithms. First, we improve the efficiency of Finite population Bayesian bootstrapping logistic regression classifier by using the rule of majority vote. Second, using simple random sampling without replacement to select a smaller number of covariates rather than the sample size and applying traditional logistic regression, we introduce the other new algorithm for high-dimensional binary classi cation. We compare the proposed algorithms with the regularized logistic regression models and two other classification algorithms, i. e., naive Bayes and K-nearest neighbors using both simulated and real data.

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APA: Copy

Zarei, Shaho, & MOHAMMADPOUR, ADEL. (2019). Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression. COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS, 7(4 (Special Issue)), 626-634. SID. https://sid.ir/paper/345703/en

Vancouver: Copy

Zarei Shaho, MOHAMMADPOUR ADEL. Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression. COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS[Internet]. 2019;7(4 (Special Issue)):626-634. Available from: https://sid.ir/paper/345703/en

IEEE: Copy

Shaho Zarei, and ADEL MOHAMMADPOUR, “Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression,” COMPUTATIONAL METHODS FOR DIFFERENTIAL EQUATIONS, vol. 7, no. 4 (Special Issue), pp. 626–634, 2019, [Online]. Available: https://sid.ir/paper/345703/en

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