Algoritma Swarm Intelligence untuk Data Berdimensi Tinggi pada Machine Learning: Review

Joan Angelina Widians, Ade Fiqri Tjikoa

Abstract


Data berdimensi tinggi berpengaruh pada learning model, ruang pencarian dan waktu komputasi, serta akurasi informasi. Banyaknya karakteristik dan dimensi tinggi, serta klasifikasi pola memerlukan pemilihan fitur atau feature selection (FS). FS dapat menghapus fitur-fitur yang berlebihan dan tidak relevan saat memilih subset fitur-fitur terkait. Swarm Intelligence (SI) banyak digunakan untuk mengatasi permasalahan data berdimensi tinggi. Secara konsep, SI dapat berarti sebagai kecerdasan kolektif yang dihasilkan dari tingkah laku kawanan agen yang terinspirasi dari alam. Makalah ini mengenai tinjauan literatur yang komprehensif tentang algoritma SI terkhusus Ant Colony Optimization, Particle Swarm Optimization, dan Grey Wolf Optimizer. Selain itu, pemaparan analisis framework SI terpadu dan investigasi berbagai pendekatan algoritma SI dalam FS. Analisis lebih lanjut menggambarkan terdapat gabungan beberapa algoritma SI di berbagai domain riset. Teknik hybrid SI ini dapat diterapkan dalam FS untuk menemukan subset fitur dengan ukuran lebih kecil dan meningkatkan performa klasifikasi dibandingkan penggunaan algoritma FS biasa. Dengan makalah ini kami menganalisis research-gap, memberikan gambaran komprehensif tentang penerapan SI dalam FS, serta mengusulkan diagram hibrid algoritma SI untuk permasalahan teks FS. Penelitian mendatang diharapkan dapat melakukan penggabungan maupun modifikasi berbagai algoritma SI menjadi suatu algoritma yang dapat meningkatkan performa sekaligus menurunkan kompleksitas waktu komputasi pada data mining dan teks mining. Harapan di masa depan, penggunaan algoritma SI semakin berkembang dan memberikan solusi yang efektif dan efisien di era big data.

Keywords


Ant Colony Optimization ; Feature Selection ; Grey Wolf Optimizer; Particle Swarm Optimization; Swarm Intelligence

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References


V. Bijalwan, P. Kumari, J. Pascual, and V. B. Semwal, “Machine learning approach for text and document mining,” arXiv Prepr. arXiv1406.1580, 2014.

S. Suyanto, “Swarm Intelligence Komputasi Modern untuk Optimasi dan Big Data Mining,” Inform. Bandung, 2017.

K. Menghour and L. Souici-Meslati, “Hybrid ACO-PSO based approaches for feature selection,” Int J Intell Eng Syst, vol. 9, no. 3, pp. 65–79, 2016.

N. Chopra, G. Kumar, and S. Mehta, “Hybrid GWO-PSO algorithm for solving convex economic load dispatch problem,” Int J Res Adv Technol, vol. 4, no. 6, pp. 37–41, 2016.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.

E. Indramaya and S. Suyanto, “Comparative Study of Recent Swarm Algorithms for Continuous Optimization,” Procedia Comput. Sci., vol. 179, no. 2019, pp. 685–695, 2021, doi: 10.1016/j.procs.2021.01.056.

J. Yousef, A. Youssef, and A. Keshk, “A Hybrid Swarm Intelligence Based Feature Selection Algorithm for High Dimensional Datasets,” IJCI. Int. J. Comput. Inf., vol. 0, no. 0, pp. 0–0, 2021, doi: 10.21608/ijci.2021.62499.1040.

I. Cholissodin and E. Riyandani, “Swarm Intelligence,” Malang Fak. Ilmu Komput. Univ. Brawijaya, 2016.

L. Brezočnik, I. Fister, and V. Podgorelec, “Swarm intelligence algorithms for feature selection: A review,” Appl. Sci., vol. 8, no. 9, 2018, doi: 10.3390/app8091521.

S. Mirjalili, “Ant colony optimisation,” Stud. Comput. Intell., vol. 780, no. November, pp. 33–42, 2019, doi: 10.1007/978-3-319-93025-1_3.

K. Tri Basuki and W. Joan Angelina, “Simulation and Visualization of TSP Using Ant Colony Optimization,” J. Data Sci., vol. 2023, no. 22, pp. 1–10, 2023.

J. A. Widians, R. Wardoyo, and S. Hartati, “Feature selection based on chi-square and ant colony optimization for multi-label classification,” Int. J. Electr. Comput. Eng., vol. 14, no. 3, p. 3303, 2024, doi: 10.11591/ijece.v14i3.pp3303-3312.

S. Kashef and H. Nezamabadi-pour, “An advanced ACO algorithm for feature subset selection,” Neurocomputing, vol. 147, pp. 271–279, 2015.

N. Singh and S. B. Singh, “Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance,” J. Appl. Math., vol. 2017, 2017.

C. Vidyadhari, N. Sandhya, and P. Premchand, “Particle grey wolf optimizer (pgwo) algorithm and semantic word processing for automatic text clustering,” Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., vol. 27, no. 02, pp. 201–223, 2019.

H. Faris, I. Aljarah, M. A. Al-Betar, and S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications,” Neural Comput. Appl., vol. 30, pp. 413–435, 2018.

Q. Al-Tashi, S. J. A. Kadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Binary optimization using hybrid grey wolf optimization for feature selection,” Ieee Access, vol. 7, pp. 39496–39508, 2019.

J. A. Widians, R. Wardoyo, and S. Hartati, “A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), 2022, pp. 1–7.

R. Purushothaman, S. P. Rajagopalan, and G. Dhandapani, “Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering,” Appl. Soft Comput., vol. 96, p. 106651, 2020.

M. A. M. Shaheen, H. M. Hasanien, and A. Alkuhayli, “A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution,” Ain Shams Eng. J., vol. 12, no. 1, pp. 621–630, 2021.

E.-S. El-Kenawy and M. Eid, “Hybrid gray wolf and particle swarm optimization for feature selection,” Int. J. Innov. Comput. Inf. Control, vol. 16, no. 3, pp. 831–844, 2020.

J. Žižka, F. Dařena, and A. Svoboda, Text mining with machine learning: principles and techniques. CRC Press, 2019.

K. S. Kyaw, S. Limsiroratana, and T. Sattayaraksa, “A comparative study of meta-heuristic and conventional search in optimization of multi-dimensional feature selection,” Int. J. Appl. Metaheuristic Comput., vol. 13, no. 1, pp. 1–34, 2022.

X. Zhou et al., “A survey on text classification and its applications,” Web Intell., vol. 18, no. 3, pp. 205–216, 2020, doi: 10.3233/WEB-200442.

O. M. Alyasiri, Y.-N. Cheah, and A. K. Abasi, “Hybrid filter-wrapper text feature selection technique for text classification,” in 2021 International Conference on Communication & Information Technology (ICICT), 2021, pp. 80–86.

O. M. Alyasiri, Y.-N. Cheah, A. K. Abasi, and O. M. Al-Janabi, “Wrapper and hybrid feature selection methods using metaheuristic algorithms for English text classification: A systematic review,” IEEE Access, vol. 10, pp. 39833–39852, 2022.

T. Baranidharan, T. Sumathi, and V. Chandra Shekar, “Weight optimized neural network using metaheuristics for the classification of large cell carcinoma and adenocarcinoma from lung imaging,” Curr. Signal Transduct. Ther., vol. 11, no. 2, pp. 91–97, 2016.

L. Wang, P. Jia, T. Huang, S. Duan, J. Yan, and L. Wang, “A novel optimization technique to improve gas recognition by electronic noses based on the enhanced krill herd algorithm,” Sensors, vol. 16, no. 8, p. 1275, 2016.

S. Kannan et al., “Preprocessing techniques for text mining,” Int. J. Comput. Sci. Commun. Networks, vol. 5, no. 1, pp. 7–16, 2014.

X. Zhou et al., “A survey on text classification and its applications,” in Web Intelligence, 2020, vol. 18, no. 3, pp. 205–216.

T. Niknam and B. Amiri, “An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis,” Appl. Soft Comput., vol. 10, no. 1, pp. 183–197, 2010.

B. Shuang, J. Chen, and Z. Li, “Study on hybrid PS-ACO algorithm,” Appl. Intell., vol. 34, no. 1, pp. 64–73, 2011.

M. S. Kiran, E. Özceylan, M. Gündüz, and T. Paksoy, “A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey,” Energy Convers. Manag., vol. 53, no. 1, pp. 75–83, 2012, doi: 10.1016/j.enconman.2011.08.004.

J. B. Jona and N. Nagaveni, “Ant-cuckoo colony optimization for feature selection in digital mammogram.,” Pak. J. Biol. Sci., vol. 17, no. 2, pp. 266–271, 2014.

M. Mahi, Ö. K. Baykan, and H. Kodaz, “A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem,” Appl. Soft Comput., vol. 30, pp. 484–490, 2015.

M. F. F. Ab Rashid, “A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem,” Assem. Autom., 2017.

M. Ghosh, R. Guha, R. Sarkar, and A. Abraham, “A wrapper-filter feature selection technique based on ant colony optimization,” Neural Comput. Appl., vol. 32, no. 12, pp. 7839–7857, 2020.

M. E. Basiri and S. Nemati, “A novel hybrid ACO-GA algorithm for text feature selection,” in 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 2561–2568.

H. Xu, X. Liu, and J. Su, “An improved grey wolf optimizer algorithm integrated with cuckoo search,” in 2017 9th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS), 2017, vol. 1, pp. 490–493.

P. Shunmugapriya and S. Kanmani, “A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid),” Swarm Evol. Comput., vol. 36, pp. 27–36, 2017.




DOI: http://dx.doi.org/10.30872/jurti.v8i1.15228

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