Araştırma Makalesi
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Weight Optimization of Weighted Naive Bayes Classifier for Efficient Classification

Yıl 2025, Cilt: 6 Sayı: 1, 51 - 63, 25.06.2025

Gamzepelin Aksoy , Murat Karabatak

https://doi.org/10.5281/zenodo.15719600

Öz

The Weighted Naive Bayes classifier is an efficient classification algorithm based on the Naive Bayes Algorithm. However, the determination of weights in this algorithm is an important problem. By using the Grid search method, the optimum solution is not reached and the algorithm works too slowly. Fast Weighted Naïve Bayes classifier is used to find weights quickly, but the performance of this algorithm is limited. Therefore, optimizing the weights has a great importance in terms of both time and achieving high performance. In this study, Genetic Algorithm and Particle Swarm Optimization methods were used to optimize the weights of the Weighted Naive Bayes classifier. The performance of Genetic Algorithm based Weighted Naive Bayes (GAW-NB) and Particle Swarm Optimization based Weighted Naive Bayes (PSOW-NB) methods were examined on five different sets with 5-fold cross validation testing method. The results of the experiments showed significant results both in terms of speed and classification performance.

Anahtar Kelimeler

Weighted Naive Bayes Classification Genetic Algorithm Particle Swarm Optimization.

Teşekkür

This article was written as a part of master’s thesis titled “Optimization of the weights of Weighted Naive Bayesian Classifier” at Firat University. Thesis no: 527473.

Kaynakça

  • Aggarwal CC, Yu PS. Data Mining Techniques for Associations, Clustering and Classification. In: Zhong N, Zhou L, editors. Methodol. Knowl. Discov. Data Min., Berlin, Heidelberg: Springer; 1999, p. 13–23. https://doi.org/10.1007/3-540-48912-6_4.
  • Ravinder B, Seeni SK, Prabhu VS, Asha P, Maniraj SP, Srinivasan C. Web Data Mining with Organized Contents Using Naive Bayes Algorithm. 2024 2nd Int. Conf. Comput. Commun. Control IC4, 2024, p. 1–6. https://doi.org/10.1109/IC457434.2024.10486403.
  • Jain J, Upadhyay SK, Nayak SK. Analyzing the Effectiveness of Machine Learning Algorithms in detecting Fake News. Comput. Commun. Intell., CRC Press; 2025.
  • Arrayyan AZ, Setiawan H, Putra KT. Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations. Semesta Tek 2024;27:28–36. https://doi.org/10.18196/st.v27i1.21008.
  • Karabatak M. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 2015;72:32–6. https://doi.org/10.1016/j.measurement.2015.04.028.
  • Aksoy G, Karabatak M. Performance Comparison of New Fast Weighted Naïve Bayes Classifier with Other Bayes Classifiers. 2019 7th Int. Symp. Digit. Forensics Secur. ISDFS, 2019, p. 1–5. https://doi.org/10.1109/ISDFS.2019.8757558.
  • Lin J, Yu J. Weighted Naive Bayes classification algorithm based on particle swarm optimization. 2011 IEEE 3rd Int. Conf. Commun. Softw. Netw., 2011, p. 444–7. https://doi.org/10.1109/ICCSN.2011.6014307.
  • Tian Z, Fong S, Tian Z, Fong S. Survey of Meta-Heuristic Algorithms for Deep Learning Training. Optim. Algorithms - Methods Appl., IntechOpen; 2016. https://doi.org/10.5772/63785.
  • Sun Y, Xue B, Zhang M, Yen GG, Lv J. Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification. IEEE Trans Cybern 2020;50:3840–54. https://doi.org/10.1109/TCYB.2020.2983860.
  • Połap D. An adaptive genetic algorithm as a supporting mechanism for microscopy image analysis in a cascade of convolution neural networks. Appl Soft Comput 2020;97:106824. https://doi.org/10.1016/j.asoc.2020.106824.
  • Cura T. A particle swarm optimization approach to clustering. Expert Syst Appl 2012;39:1582–8. https://doi.org/10.1016/j.eswa.2011.07.123.
  • Huang KY. A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowl-Based Syst 2011;24:420–6. https://doi.org/10.1016/j.knosys.2010.12.003.
  • Kotsiantis SB. Supervised Machine Learning: A Review of Classification Techniques 2007.
  • Dagdia, Zaineb Chelly, Mirchev, Miroslav. Optimization Problem - an overview 2020.
  • Alhijawi B, Awajan A. Genetic algorithms: theory, genetic operators, solutions, and applications. Evol Intell 2024;17:1245–56. https://doi.org/10.1007/s12065-023-00822-6.
  • Gen, Mitsuo, Cheng, Runwei. Foundations of Genetic Algorithms. Genet. Algorithms Eng. Optim., John Wiley & Sons, Ltd; 1999, p. 1–52. https://doi.org/10.1002/9780470172261.ch1.
  • Jain M, Saihjpal V, Singh N, Singh SB. An Overview of Variants and Advancements of PSO Algorithm. Appl Sci 2022;12:8392. https://doi.org/10.3390/app12178392.
  • Eberhart, Shi Y. Particle swarm optimization: developments, applications and resources. Proc. 2001 Congr. Evol. Comput. IEEE Cat No01TH8546, vol. 1, 2001, p. 81–6 vol. 1. https://doi.org/10.1109/CEC.2001.934374.
  • Home - UCI Machine Learning Repository n.d. https://archive.ics.uci.edu/ (accessed January 30, 2025).
  • Aha D. Tic-Tac-Toe Endgame 1991. https://doi.org/10.24432/C5688J.
  • Sharon Summers LW. Post-Operative Patient 1991. https://doi.org/10.24432/C5DG6Q.
  • Kim M-J, Han I. The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Syst Appl 2003;25:637–46. https://doi.org/10.1016/S0957-4174(03)00102-7.
  • Elter M. Mammographic Mass 2007. https://doi.org/10.24432/C53K6Z.
  • De Jong K. Learning with genetic algorithms: An overview. Mach Learn 1988;3:121–38. https://doi.org/10.1007/BF00113894.

Ağırlıklı Sade Bayes Sınıflandırıcısında Etkili Sınıflandırma İçin Ağırlıkların Optimizasyonu

Yıl 2025, Cilt: 6 Sayı: 1, 51 - 63, 25.06.2025

Gamzepelin Aksoy , Murat Karabatak

https://doi.org/10.5281/zenodo.15719600

Öz

Ağırlıklı sade bayes sınıflandırıcısı, sade bayes algoritmasına dayanan verimli bir sınıflandırma algoritmasıdır. Ancak bu algoritmada ağırlıkların belirlenmesi önemli bir problemdir. Izgara tarama yöntemi kullanılarak yapılan aramalarda genellikle en uygun çözüme ulaşılamamakta ve algoritma oldukça yavaş çalışmaktadır. Ağırlıkları hızlı bir şekilde bulmak için hızlı ağırlıklı sade bayes sınıflandırıcısı kullanılsa da bu algoritmanın performansı sınırlıdır. Bu nedenle, ağırlıkların optimize edilmesi hem zaman açısından hem de yüksek performansa ulaşmak açısından büyük önem taşımaktadır. Bu çalışmada, ağırlıklı sade bayes sınıflandırıcısının ağırlıklarını optimize etmek için genetik algoritma ve parçacık sürü optimizasyonu yöntemleri kullanılmıştır. Genetik Algoritma tabanlı Ağırlıklı Naive Bayes (GAW-NB) ve Parçacık Sürü Optimizasyonu tabanlı Ağırlıklı Naive Bayes (PSOW-NB) yöntemlerinin performansları, beş farklı veri kümesi üzerinde 5 katlı çapraz doğrulama testi ile değerlendirilmiştir. Yapılan deneylerin sonuçları, hem hız hem de sınıflandırma performansı açısından anlamlı sonuçlar ortaya koymuştur.

Anahtar Kelimeler

Ağırlıklı Sade Bayes Sınıflandırma Genetik Algoritma Parçacık Sürü Optimizasyonu.

Kaynakça

  • Aggarwal CC, Yu PS. Data Mining Techniques for Associations, Clustering and Classification. In: Zhong N, Zhou L, editors. Methodol. Knowl. Discov. Data Min., Berlin, Heidelberg: Springer; 1999, p. 13–23. https://doi.org/10.1007/3-540-48912-6_4.
  • Ravinder B, Seeni SK, Prabhu VS, Asha P, Maniraj SP, Srinivasan C. Web Data Mining with Organized Contents Using Naive Bayes Algorithm. 2024 2nd Int. Conf. Comput. Commun. Control IC4, 2024, p. 1–6. https://doi.org/10.1109/IC457434.2024.10486403.
  • Jain J, Upadhyay SK, Nayak SK. Analyzing the Effectiveness of Machine Learning Algorithms in detecting Fake News. Comput. Commun. Intell., CRC Press; 2025.
  • Arrayyan AZ, Setiawan H, Putra KT. Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations. Semesta Tek 2024;27:28–36. https://doi.org/10.18196/st.v27i1.21008.
  • Karabatak M. A new classifier for breast cancer detection based on Naïve Bayesian. Measurement 2015;72:32–6. https://doi.org/10.1016/j.measurement.2015.04.028.
  • Aksoy G, Karabatak M. Performance Comparison of New Fast Weighted Naïve Bayes Classifier with Other Bayes Classifiers. 2019 7th Int. Symp. Digit. Forensics Secur. ISDFS, 2019, p. 1–5. https://doi.org/10.1109/ISDFS.2019.8757558.
  • Lin J, Yu J. Weighted Naive Bayes classification algorithm based on particle swarm optimization. 2011 IEEE 3rd Int. Conf. Commun. Softw. Netw., 2011, p. 444–7. https://doi.org/10.1109/ICCSN.2011.6014307.
  • Tian Z, Fong S, Tian Z, Fong S. Survey of Meta-Heuristic Algorithms for Deep Learning Training. Optim. Algorithms - Methods Appl., IntechOpen; 2016. https://doi.org/10.5772/63785.
  • Sun Y, Xue B, Zhang M, Yen GG, Lv J. Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification. IEEE Trans Cybern 2020;50:3840–54. https://doi.org/10.1109/TCYB.2020.2983860.
  • Połap D. An adaptive genetic algorithm as a supporting mechanism for microscopy image analysis in a cascade of convolution neural networks. Appl Soft Comput 2020;97:106824. https://doi.org/10.1016/j.asoc.2020.106824.
  • Cura T. A particle swarm optimization approach to clustering. Expert Syst Appl 2012;39:1582–8. https://doi.org/10.1016/j.eswa.2011.07.123.
  • Huang KY. A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowl-Based Syst 2011;24:420–6. https://doi.org/10.1016/j.knosys.2010.12.003.
  • Kotsiantis SB. Supervised Machine Learning: A Review of Classification Techniques 2007.
  • Dagdia, Zaineb Chelly, Mirchev, Miroslav. Optimization Problem - an overview 2020.
  • Alhijawi B, Awajan A. Genetic algorithms: theory, genetic operators, solutions, and applications. Evol Intell 2024;17:1245–56. https://doi.org/10.1007/s12065-023-00822-6.
  • Gen, Mitsuo, Cheng, Runwei. Foundations of Genetic Algorithms. Genet. Algorithms Eng. Optim., John Wiley & Sons, Ltd; 1999, p. 1–52. https://doi.org/10.1002/9780470172261.ch1.
  • Jain M, Saihjpal V, Singh N, Singh SB. An Overview of Variants and Advancements of PSO Algorithm. Appl Sci 2022;12:8392. https://doi.org/10.3390/app12178392.
  • Eberhart, Shi Y. Particle swarm optimization: developments, applications and resources. Proc. 2001 Congr. Evol. Comput. IEEE Cat No01TH8546, vol. 1, 2001, p. 81–6 vol. 1. https://doi.org/10.1109/CEC.2001.934374.
  • Home - UCI Machine Learning Repository n.d. https://archive.ics.uci.edu/ (accessed January 30, 2025).
  • Aha D. Tic-Tac-Toe Endgame 1991. https://doi.org/10.24432/C5688J.
  • Sharon Summers LW. Post-Operative Patient 1991. https://doi.org/10.24432/C5DG6Q.
  • Kim M-J, Han I. The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Syst Appl 2003;25:637–46. https://doi.org/10.1016/S0957-4174(03)00102-7.
  • Elter M. Mammographic Mass 2007. https://doi.org/10.24432/C53K6Z.
  • De Jong K. Learning with genetic algorithms: An overview. Mach Learn 1988;3:121–38. https://doi.org/10.1007/BF00113894.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sorgu İşleme ve Optimizasyon, Veri Madenciliği ve Bilgi Keşfi
Bölüm Araştırma Makaleleri
Yazarlar

Gamzepelin Aksoy 0000-0002-5328-2983

Murat Karabatak 0000-0002-6719-7421

Yayımlanma Tarihi 25 Haziran 2025
Gönderilme Tarihi 2 Haziran 2025
Kabul Tarihi 13 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 1

Kaynak Göster

Vancouver Aksoy G, Karabatak M. Weight Optimization of Weighted Naive Bayes Classifier for Efficient Classification. BUTS. 2025;6(1):51-63.
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