Araştırma Makalesi

Performance of Various Naive Bayes Algorithms on Employee Attrition Detection

Cilt: 2 Sayı: 1 1 Temmuz 2025
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Performance of Various Naive Bayes Algorithms on Employee Attrition Detection

Öz

Since companies aim to increase profits, the company's resources must be used correctly. Two of the resources that human resources should consider important for the company are time and the continuation of talented employees in the workplace. In the case of loss of talented employees, training given to new workers requires wages and time. This situation shows that loss of employees is an important problem in company policy. This study aims to automatically detect employee attrition with machine learning algorithms. Naive Bayes classifiers are one of the successful machine learning algorithms used in many different fields such as text processing. Therefore, in this study, supervised classification has performed on the dataset with 5 different Naive Bayes algorithms in determining the employment loss. Gaussian Naive Bayes and Categorical Naive Bayes show the most successful results in determining the loss of workers.

Anahtar Kelimeler

Kaynakça

  1. Abbas, M., Memon, K. A., Jamali, A. A., Memon, S., & Ahmed, A. (2019). Multinomial Naive Bayes classification model for sentiment analysis. IJCSNS Int. J. Comput. Sci. Netw. Secur, 19(3), 62.
  2. Alduayj, S. S., & Rajpoot, K. (2018, November). Predicting employee attrition using machine learning. In 2018 IEEE International Conference on Innovations in Information Technology (IIT) (pp. 93-98).
  3. Alsubaie, F., & Aldoukhi, M. (2024). Using machine learning algorithms with improved accuracy to analyze and predict employee attrition. Decision Science Letters, 13(1), 1-18. doi: 10.5267/j.dsl.2023.12.006
  4. Anonim. (2012). US Department of agriculture nutrient database for standard reference, Release 14. URL: http://www.nal.usda.gov/fnic/foodcomp (accessed date: March 23, 2012).
  5. Atalar, M.N. and Türkan, F. (2018). Identification of chemical components from the Rhizomes of Acorus calamus L. with gas chromatography-tandem mass spectrometry (GC-MS\MS). Journal of the Institute of Science and Technology, 8(4), 181-187. doi: 10.21597/jist.433743
  6. Dimitoglou, G., Adams, J. A., & Jim, C. M. (2012). Comparison of the C4. 5 and a Naïve Bayes classifier for the prediction of lung cancer survivability. arXiv preprint arXiv:1206.1121.
  7. Fallucchi, F., Coladangelo, M., Giuliano, R., & William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9(4), 86. doi: 10.3390/computers9040086
  8. Krishna, S., & Sidharth, S. (2024). HR Analytics: Analysis of Employee Attrition Using Perspectives from Machine Learning. In Flexibility, Resilience and Sustainability (pp. 267-286). Singapore: Springer Nature Singapore.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Denetimli Öğrenme, Makine Öğrenmesi Algoritmaları, Sınıflandırma algoritmaları

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

1 Temmuz 2025

Gönderilme Tarihi

9 Aralık 2024

Kabul Tarihi

11 Ocak 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 2 Sayı: 1

Kaynak Göster

APA
Gemci, F. (2025). Performance of Various Naive Bayes Algorithms on Employee Attrition Detection. ADÜ Fen ve Mühendislik Bilimleri Dergisi, 2(1), 1-6. https://izlik.org/JA67KX45PH
AMA
1.Gemci F. Performance of Various Naive Bayes Algorithms on Employee Attrition Detection. adufmbd. 2025;2(1):1-6. https://izlik.org/JA67KX45PH
Chicago
Gemci, Fahriye. 2025. “Performance of Various Naive Bayes Algorithms on Employee Attrition Detection”. ADÜ Fen ve Mühendislik Bilimleri Dergisi 2 (1): 1-6. https://izlik.org/JA67KX45PH.
EndNote
Gemci F (01 Temmuz 2025) Performance of Various Naive Bayes Algorithms on Employee Attrition Detection. ADÜ Fen ve Mühendislik Bilimleri Dergisi 2 1 1–6.
IEEE
[1]F. Gemci, “Performance of Various Naive Bayes Algorithms on Employee Attrition Detection”, adufmbd, c. 2, sy 1, ss. 1–6, Tem. 2025, [çevrimiçi]. Erişim adresi: https://izlik.org/JA67KX45PH
ISNAD
Gemci, Fahriye. “Performance of Various Naive Bayes Algorithms on Employee Attrition Detection”. ADÜ Fen ve Mühendislik Bilimleri Dergisi 2/1 (01 Temmuz 2025): 1-6. https://izlik.org/JA67KX45PH.
JAMA
1.Gemci F. Performance of Various Naive Bayes Algorithms on Employee Attrition Detection. adufmbd. 2025;2:1–6.
MLA
Gemci, Fahriye. “Performance of Various Naive Bayes Algorithms on Employee Attrition Detection”. ADÜ Fen ve Mühendislik Bilimleri Dergisi, c. 2, sy 1, Temmuz 2025, ss. 1-6, https://izlik.org/JA67KX45PH.
Vancouver
1.Fahriye Gemci. Performance of Various Naive Bayes Algorithms on Employee Attrition Detection. adufmbd [Internet]. 01 Temmuz 2025;2(1):1-6. Erişim adresi: https://izlik.org/JA67KX45PH