A new metaheuristic approach for feature selection based on immune plasma algorithm: IPAbFS

Abstract

Feature selection (FS) is a critical preprocessing step in machine learning that enhances model performance by removing irrelevant and redundant features. Although various metaheuristic algorithms have been applied to this process, research continues to find more successful methods for high-dimensional data. This study proposes IPAbFS, a new feature selection approach based on the Immune Plasma Algorithm (IPA). The performance of the proposed IPAbFS has been comprehensively evaluated on 39 different datasets across various domains. To ensure the reliability of the results, 10-fold cross-validation was applied using K-Nearest Neighbors (KNN), Decision Tree (DT), and Support Vector Machines (SVM) classifiers. The experimental results show that IPAbFS significantly outperforms ten competing metaheuristic FS algorithms in terms of classification accuracy and feature reduction.

Keywords

• Feature selection
• Immune plasma algorithm
• Machine learning
• Optimization
• Classification

Öznitelik seçimi için immün plazma algoritmasına dayalı yeni bir metasezgisel yaklaşım: IPAbFS

Abstract

Özellik seçimi (FS), makine öğreniminde, alakasız ve gereksiz özellikleri kaldırarak model performansını artıran kritik bir ön işleme adımıdır. Bu işleme çeşitli metaheuristik algoritmalar uygulanmış olsa da yüksek boyutlu veriler için daha başarılı yöntemler bulmak üzere araştırmalar devam etmektedir. Bu çalışma, İmmün Plazma Algoritması (IPA) temelinde yeni bir özellik seçimi yaklaşımı olan IPAbFS'yi önermektedir. Önerilen IPAbFS'nin performansı, çeşitli alanlarda 39 farklı veri seti üzerinde kapsamlı bir şekilde değerlendirilmiştir. Sonuçların güvenilirliğini sağlamak için, K-En Yakın Komşular (KNN), Karar Ağacı (DT) ve Destek Vektör Makineleri (SVM) sınıflandırıcıları kullanılarak 10 kat çapraz doğrulama uygulanmıştır. Deney sonuçları, IPAbFS'nin sınıflandırma doğruluğu ve özellik azaltma açısından on adet rakip metaheuristik FS algoritmasından önemli ölçüde daha iyi performans sergilediğini göstermektedir.

Keywords

• Öznitelik seçimi
• İmmün plazma algoritması
• Makine öğrenmesi
• Optimizasyon
• Sınıflandırma

References

1. [1] Nazar NB, Senthilkumar R. “An online approach for feature selection for classification in big data”. Turkish Journal of Electrical Engineering and Computer Sciences, 25(1), 163-171, 2017.
2. [2] Beştaş MŞ, Batur Dinler Ö. “Comparative analysis of malicious android-based software detection with trending metaheuristic algorithms”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 31(1), 98–115, 2025.
3. [3] Atik S, Dalyan T. “Feature selection optimization with filtering and wrapper methods: two disease classification cases”. Turkish Journal of Electrical Engineering and Computer Sciences, 31(7), 1329-1342, 2023.
4. [4] Preeti, Deep K. “A random walk grey wolf optimizer based on dispersion factor for feature selection on chronic disease prediction”. Expert Systems with Applications, 206, 117864, 2022.
5. [5] Srinivasa Rao PC, Sravan Kumar AJ, Niyaz Q, Sidike P, Devabhaktuni VK. “Binary chemical reaction optimization based feature selection techniques for machine learning classification problems”. Expert Systems with Applications, 167, 114169, 2021.
6. [6] Wei G, Zhao J, Feng Y, He A, Yu J. “A novel hybrid feature selection method based on dynamic feature importance”. Applied Soft Computing, 93, 106337, 2020.
7. [7] Huda RK, Banka H. “Efficient feature selection methods using PSO with fuzzy rough set as fitness function”.Soft Computing, 26(5), 2501–2521, 2022.
8. [8] Li J, Kang H, Sun G, Feng T, Li W, Zhang W, Ji B. “IBDA: Improved binary dragonfly algorithm with evolutionary population dynamics and adaptive crossover for feature selection”. IEEE Access, 8, 108032–108051, 2020.

9. [9] Aljarah I, Faris H, Heidari AA, Mafarja MM, Al-Zoubi AM, Castillo PA, Merelo JJ. “A robust multi-objective feature selection model based on local neighborhood multi-verse optimization”. IEEE Access, 9, 100009–100028, 2021.
10. [10] Raymer ML, Punch WF, Goodman ED, Kuhn LA, Jain AK. “Dimensionality reduction using genetic algorithms”. IEEE Transactions on Evolutionary Computation, 4(2), 164–171, 2000.
11. [11] Cui X, Beaver JM, Charles JS, Potok TE. “Dimensionality reduction particle swarm algorithm for high dimensional clustering”. IEEE Swarm Intelligence Symposium (SIS 2008), St. Louis, MO, USA, 21–23 September 2008.
12. [12] Prasartvit T, Kaewkamnerdpong B, Achalakul T. “Dimensional reduction based on artificial bee colony for classification problems”. Lecture Notes in Computer Science, 6840, 168–175, 2011.
13. [13] Martinović G, Bajer D, Zorić B. “A differential evolution approach to dimensionality reduction for classification needs”. International Journal of Applied Mathematics and Computer Science, 24(1), 111–122, 2014.
14. [14] Emary E, Zawbaa HM, Grosan C, Hassenian AE. “Feature subset selection approach by gray-wolf optimization”. Advances in Intelligent Systems and Computing, 334, 1–13, 2015.
15. [15] Deriche M. “Feature selection using ant colony optimization”. 6th International Multi-Conference on Systems, Signals and Devices (SSD 2009), Djerba, Tunisia, 23–26 March 2009.
16. [16] Sevinç E, Dökeroğlu T. “A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines”. Turkish Journal of Electrical Engineering and Computer Sciences, 27(2), 1523-1533, 2019.
17. [17] Aslan S., Demirci S. “Immune plasma algorithm: A novel meta-heuristic for optimization problems”. IEEE Access, 8, 220227–220245, 2020.
18. [18] Aslan S. “An immune plasma algorithm with a modified treatment schema for UCAV path planning”. Engineering Applications of Artificial Intelligence, 112, 104789, 2022.
19. [19] Aslan S, Erkin T. “An immune plasma algorithm based approach for UCAV path planning”. Journal of King Saud University - Computer and Information Sciences, 35(1), 56–69, 2023.
20. [20] Aslan S, Demirci S, Oktay T, Yesilbas E. “Percentile-based adaptive immune plasma algorithm and its application to engineering optimization”. Biomimetics, 8(6), 486, 2023.
21. [21] Aslan S, Demirci S. “An immune plasma algorithm with Q-learning based pandemic management for path planning of unmanned aerial vehicles”. Egyptian Informatics Journal, 26, 100468, 2024.
22. [22] Aslan S, Oktay T. “Path planning of an unmanned combat aerial vehicle with an extended-treatment-approach-based immune plasma algorithm”. Aerospace, 10(5), 487, 2023.
23. [23] Aslan S, Rohacs D, Yıldız M, Kale U. “A three-dimensional UCAV path planning approach based on immune plasma algorithm”. International Journal of Computational Intelligence Systems, 16(1), 112, 2023.
24. [24] Kısa M, Aslan S, Demirci S. “Immün plazma algoritması ile bilişsel radyo ağlarında çok kullanıcılı kanal atama probleminin çözülmesi”. Avrupa Bilim ve Teknoloji Dergisi, 29, 304–310, 2021.
25. [25] Arslan S. “Zaman serisi tahmin probleminin immün plazma programlama kullanılarak çözülmesi”.Avrupa Bilim ve Teknoloji Dergisi, 29, 219–224, 2021.
26. [26] Aslan S, Demirci S. “Performance investigation of parallel immune plasma algorithm”. 2021 International Conference on Innovations in Intelligent Systems and Applications (INISTA), Kocaeli, Turkey, 25–27 August 2021.
27. [27] Aslan S, Demirci S. “An improved immune plasma algorithm with a regional pandemic restriction”. Signal, Image and Video Processing, 16(8), 2093–2101, 2022.
28. [28] Oguz O, Badem H. “A new metaheuristic approach to diagnosis of Parkinson’s disease through audio signals”. Elektronika ir Elektrotechnika, 30(4), 68–75, 2024.
29. [29] MiarNaeimi F, Azizyan G, Rashki M. “Horse herd optimization algorithm: A nature-inspired algorithm for high-dimensional optimization problems”. Knowledge-Based Systems, 213, 106711, 2021.
30. [30] Elmanakhly DA, Saleh MM, Rashed EA, Abdel-Basset M. “Binhoa: Efficient binary horse herd optimization method for feature selection: Analysis and validations”.IEEE Access, 10, 26795–26816, 2022.
31. [31] Hayyolalam V, Kazem AAP. “Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems”. Engineering Applications of Artificial Intelligence, 87, 103249, 2020.
32. [32] Hu G, Du B, Wang X, Wei G. “An enhanced black widow optimization algorithm for feature selection”. Knowledge-Based Systems, 235, 107638, 2022.
33. [33] Saremi S, Mirjalili S, Lewis A. “Grasshopper optimisation algorithm: Theory and application”. Advances in Engineering Software, 105, 30–47, 2017.
34. [34] Xu Z, Gui W, Heidari AA, Liang G, Chen H, Wu C, Turabieh H, Mafarja M. “Spiral motion mode embedded grasshopper optimization algorithm: Design and analysis”. IEEE Access, 9, 71104–71132, 2021.
35. [35] Kılıç F, Kaya Y, Yildirim S. “A novel multi population based particle swarm optimization for feature selection”. Knowledge-Based Systems, 219, 106894, 2021.
36. [36] Al-Wajih R, Abdulkadir SJ, Aziz N, Al-Tashi Q, Talpur N. “Hybrid binary grey wolf with harris hawks optimizer for feature selection”. IEEE Access, 9, 31662–31677, 2021.
37. [37] Elgamal Z, Sabri AQM, Tubishat M, Tbaishat D, Makhadmeh SN, Alomari OA. “Improved reptile search optimization algorithm using chaotic map and simulated annealing for feature selection in medical field”. IEEE Access, 10, 51428–51446, 2022.
38. [38] Sharafaddini AM, Mansouri N. “A binary chaotic transient search optimization algorithm for enhancing feature selection”. Arabian Journal for Science and Engineering, 50(2), 679-702, 2025.
39. [39] Akalın F, Yumuşak N. “Classification of acute leukaemias with a hybrid use of feature selection algorithms and deep learning-based architectures”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 29(3), 256–263, 2023.
40. [40] Elmanakhly DA, Saleh MM, Rashed EA. "An Improved Equilibrium Optimizer Algorithm for Features Selection: Methods and Analysis". IEEE Access, 9, 120309-120327, 2021.
41. [41] Feng J, Gong Z. “A novel feature selection method with neighborhood rough set and improved particle swarm optimization”. IEEE Access, 10, 33301–33312, 2022.
42. [42] Tubishat M, Alswaitti M, Mirjalili S, Al-Garadi MA, Alrashdan MT, Rana TA. “Dynamic butterfly optimization algorithm for feature selection”. IEEE Access, 8, 194303–194314, 2020.
43. [43] Ewees AA, Gaheen MA, Yaseen ZM, Ghoniem RM. “Grasshopper optimization algorithm with crossover operators for feature selection and solving engineering problems”. IEEE Access, 10, 23304–23320, 2022.