An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model

Md Kabirul Islam; Mirza Mohtashim Alam; Md Baharul Islam; Karishma Mohiuddin; Amit Kishor Das; Md Shamsul Kaonain

doi:10.1007/978-981-13-1813-9_27

An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model

Md Kabirul Islam, Mirza Mohtashim Alam, Md Baharul Islam, Karishma Mohiuddin, Amit Kishor Das, Md Shamsul Kaonain

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it’s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature’s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.

Original language	English
Title of host publication	Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers
Editors	Tuncer Ören, P. K. Gupta, Jan Flusser, Vipin Tyagi, Mayank Singh
Publisher	Springer Verlag
Pages	269-278
Number of pages	10
ISBN (Print)	9789811318122
DOIs	https://doi.org/10.1007/978-981-13-1813-9_27
Publication status	Published - 2018
Externally published	Yes
Event	2nd International Conference on Advances in Computing and Data Sciences, ICACDS 2018 - Dehradun, India Duration: 20 Apr 2018 → 21 Apr 2018

Publication series

Name	Communications in Computer and Information Science
Volume	906
ISSN (Print)	1865-0929

Conference

Conference	2nd International Conference on Advances in Computing and Data Sciences, ICACDS 2018
Country/Territory	India
City	Dehradun
Period	20/04/18 → 21/04/18

Keywords

Classifier
Dimensionality reduction
LDA
PCA
Random forest

Access to Document

10.1007/978-981-13-1813-9_27

Cite this

Islam, M. K., Alam, M. M., Islam, M. B., Mohiuddin, K., Das, A. K., & Kaonain, M. S. (2018). An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. In T. Ören, P. K. Gupta, J. Flusser, V. Tyagi, & M. Singh (Eds.), Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers (pp. 269-278). (Communications in Computer and Information Science; Vol. 906). Springer Verlag. https://doi.org/10.1007/978-981-13-1813-9_27

Islam, Md Kabirul ; Alam, Mirza Mohtashim ; Islam, Md Baharul et al. / An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers. editor / Tuncer Ören ; P. K. Gupta ; Jan Flusser ; Vipin Tyagi ; Mayank Singh. Springer Verlag, 2018. pp. 269-278 (Communications in Computer and Information Science).

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abstract = "This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it{\textquoteright}s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature{\textquoteright}s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.",

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author = "Islam, {Md Kabirul} and Alam, {Mirza Mohtashim} and Islam, {Md Baharul} and Karishma Mohiuddin and Das, {Amit Kishor} and Kaonain, {Md Shamsul}",

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Islam, MK, Alam, MM, Islam, MB, Mohiuddin, K, Das, AK & Kaonain, MS 2018, An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. in T Ören, PK Gupta, J Flusser, V Tyagi & M Singh (eds), Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers. Communications in Computer and Information Science, vol. 906, Springer Verlag, pp. 269-278, 2nd International Conference on Advances in Computing and Data Sciences, ICACDS 2018, Dehradun, India, 20/04/18. https://doi.org/10.1007/978-981-13-1813-9_27

An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. / Islam, Md Kabirul; Alam, Mirza Mohtashim; Islam, Md Baharul et al.
Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers. ed. / Tuncer Ören; P. K. Gupta; Jan Flusser; Vipin Tyagi; Mayank Singh. Springer Verlag, 2018. p. 269-278 (Communications in Computer and Information Science; Vol. 906).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Islam, Md Kabirul

AU - Alam, Mirza Mohtashim

AU - Islam, Md Baharul

AU - Mohiuddin, Karishma

AU - Das, Amit Kishor

AU - Kaonain, Md Shamsul

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd 2018.

PY - 2018

Y1 - 2018

N2 - This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it’s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature’s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.

AB - This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it’s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature’s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.

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ER -

Islam MK, Alam MM, Islam MB, Mohiuddin K, Das AK, Kaonain MS. An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model. In Ören T, Gupta PK, Flusser J, Tyagi V, Singh M, editors, Advances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers. Springer Verlag. 2018. p. 269-278. (Communications in Computer and Information Science). doi: 10.1007/978-981-13-1813-9_27

An adaptive feature dimensionality reduction technique based on random forest on employee turnover prediction model

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Keywords

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