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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach
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Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach

机译：Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach

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摘要

In this article, we compare autometrics and machine learning techniques including Minimax Concave Penalty (MCP), Elastic Smoothly Clipped Absolute Deviation (E-SCAD), and Adaptive Elastic Net (AEnet). For simulation experiments, three kinds of scenarios are considered by allowing the multicollinearity, heteroscedasticity, and autocorrelation conditions with varying sample sizes and the varied number of covariates. We found that all methods show improved their performance for a large sample size. In the presence of low and moderate multicollinearity and low and moderate autocorrelation, the considered methods retain all relevant variables. However, for low and moderate multicollinearity, excluding AEnet, all methods keep many irrelevant predictors as well. In contrast, under low and moderate autocorrelation, along with AEnet, the Autometrics retain less irrelevant predictors. Considering the case of extreme multicollinearity, AEnet retains more than 93 percent correct variables with an outstanding gauge (zero percent). However, the potency of remaining techniques, specifically MCP and E-SCAD, tends towards unity with augmenting sample size but capturing massive irrelevant predictors. Similarly, in case of high autocorrelation, E-SCAD has shown good performance in the selection of relevant variables for a small sample, while in gauge, Autometrics and AEnet are performed better and often retained less than 5 percent irrelevant variables. In the presence of heteroscedasticity, all techniques often hold all relevant variables but also suffer from overspecification problems except AEnet and Autometrics which circumvent the irrelevant predictors and establish the true model precisely. For an empirical application, we take into account the workers’ remittance data for Pakistan along its twenty-seven determinants spanning from 1972 to 2020 for Pakistan. The AEnet selected thirteen relevant covariates of workers’ remittance while E-SCAD and MCP suffered from an overspecification problem. Hence, the policymakers and practitioners should focus on the relevant variables selected by AEnet to improve workers' remittance in the case of Pakistan. In this regard, the Pakistan government has devised policies that make it easy to transfer remittances legally and mitigate the cost of transferring remittances from abroad. The AEnet approach can help policymakers arrive at relevant variables in the presence of a huge set of covariates, which in turn produce accurate predictions.

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