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Using synthetic data and dimensionality reduction in high-dimensional classification via logistic regression | ||
| Computational Methods for Differential Equations | ||
| مقاله 13، دوره 7، Issue 4 (Special Issue)، آبان 2019، صفحه 626-634 اصل مقاله (114.93 K) | ||
| نوع مقاله: Research Paper | ||
| نویسندگان | ||
| Shaho Zarei* 1؛ Adel Mohammadpour2 | ||
| 1Department of Statistics, Faculty of Science, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran | ||
| چکیده | ||
| 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 classification. 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. | ||
| کلیدواژهها | ||
| High-dimensional classification؛ Logistic regression classifier؛ Dimensionality reduction؛ Random forest؛ Finite population Bayesian bootstrapping | ||
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آمار تعداد مشاهده مقاله: 393 تعداد دریافت فایل اصل مقاله: 291 |
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