Bilal ALATAŞ, Suna YILDIRIM, Güngör YILDIRIM

Anlaşılabilir Sınıflandırma Kurallarının Ayçiçeği Optimizasyon Algoritması ile Otomatik Keşfi

Kural madenciliği, veri madenciliğinin önemli alt dallarından biri olup günümüzde hala üzerinde çalışılan sıcak bir çalışma alanıdır. Nicel nitelik içeren veri setleri üzerinde çalışan standart sınıflandırma yöntemleri genellikle ön işlem aşamalarına ihtiyaç duyarlar. Bu yapılan ayrıklaştırmalar ise başarım kaybına yol açabilmektedir. Buna ek olarak standart sınıflandırma algoritmalarının kara-kutu yapılarından dolayı kural açıklanabilirlikleri iyi değildir. Bu noktada, sürekli veriler ile çalışabilen optimizasyon algoritmaları, bu dezavantajların üstesinden gelebilir. Bu çalışmada, son yılların başarılı optimizasyon algoritmalarından olan Ayçiçeği Optimizasyon algoritmasını kullanarak verimli bir kural madenciliği gerçekleştirilmiştir. Bunun için, farklı bir temsil biçimi kullanan aday bitki yapısı, bu optimizasyon algoritmasına uyarlanmıştır. Arama uzayı olarak üç farklı disipline ait veri seti kullanılmış ve yöntemin başarımını gözlemlemek için iyi bilinen beş farklı sınıflandırma algoritmasına ait sonuçlar paylaşılmıştır. Elde edilen sonuçlar, optimizasyon temelli yaklaşım ile veri setleri üzerinde herhangi bir ön işlem yapmaya gerek kalmadan açıklanabilir kurallar üretilebileceğini ispatlamaktadır.

Anahtar Kelimeler:

Optimizasyon, Kural madenciliği, Ayçiçeği optimizasyon algoritması, Doğruluk

Automatic Discovery of Comprehensible Classification Rules with Sunflower Optimization Algorithm

Rule mining is one of the important sub-branches of data mining, and it is still a hot topic study area for researchers. Standard classification methods usually require pre-processing steps when working with datasets containing quantitative attributes. On the other hand, discretization at these stages may lead to a loss of performance and accuracy. In addition, due to the black-box nature of standard classification algorithms, rule explicability is not good. At this point, optimization algorithms that can work with continuous data can overcome these disadvantages. This study focuses on rule mining using the Sunflower Optimization algorithm, one of the successful optimization algorithms of recent years. For this, the candidate plant structure using a different representation format was adapted to this optimization algorithm. Data sets from three different disciplines were used as the search space, and the results of five different well-known classification algorithms were shared for performance observations. The results obtained proved that, with the optimization-based approach, explicable rules can be produced without any pre-processing on the data sets.

Keywords:

Optimization, Rule Mining, Sunflower Optimization Algorithm, Accuracy,

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[1] Savargiv M, Masoumi B, Keyvanpour MR. A new ensemble learning method based on learning automata. Journal of Ambient Intelligence and Humanized Computing.2020; 1-16.
[2] Liu J, Chi Y, Liu Z, He S. Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps. CAAI Transactions on Intelligence Technology. 2019; 4(1): 24–12.
[3] He C, Ma M, Wang P. Extract Interpretability-Accuracy balanced Rules from Artificial Neural Networks: A Review. Neurocomputing. 2020; 387(C):346-12.
[4] Kiziloluk S, Alatas B. Automatic mining of numerical classification rules with parliamentary optimization algorithm. Advances in Electrical and Computer Engineering. 2015; 15(4): 17-8.
[5] Phoungphol P, Zhang Y, Zhao Y. Robust multiclass classification for learning from imbalanced biomedical data. Tsinghua Science and technology. 2012; 17(6): 619-9.
[6] Gündoğan KK, Alataş B, Karci A. Mining Classification Rules by Using Genetic Algorithms with Nonrandom Initial Population and Uniform Operator. Turk J Elec Engin. 2004;12(1): 43-9.
[7] Pourpanaha F, Limb CP, Saleha JM. A hybrid model of fuzzy ARTMAP and genetic algorithm for data classification and rule extraction. Expert Systems with Applications. 2016;49:74-11.
[8] Tripathy S, Hota S, Satapathy P. MTACO-Miner: Modified Threshold Ant Colony Optimization Miner for Classification Rule Mining. Emerging Research in Computing, Information, Communication and Applications. Elsevier; 2013.p.1-5.
[9] Taboada K, Mabu S, Gonzales E, Shimada K, Hirasawa K. Fuzzy Classification Rule Mining Based on Genetic Network Programming Algorithm. IEEE Conference on Systems, Man, and Cybernetics. USA: 2009. p. 3960-6.
[10] Dehuri S, Cho S. Multi-objective Classification Rule Mining Using Gene Expression Programming. Third International Conference on Convergence and Hybrid Information Technology. Korea:2008. p. 755-7.
[11] Zhong-Yang X, Lei Z, Yu-Fang Z. A Classification Rule Mining Method Using Hybrid Genetic Algorithms. IEEE Region 10 Conference Tencon. Thailand: 2004. p.207-4.
[12] Ghobaei‐Arani M, Souri A, Safara F, Norouzi M. An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Transactions on Emerging Telecommunications Technologies. 2019; 31(1):1-14.
[13] Safara F, Mohammed AS, Potrus MY, Ali S, Tho QT, Souri A, et al. An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network. IEEE Access.2020; 8:48428-10.
[14] Yildirim G, Alatas B. (2021), New Adaptive Intelligent Grey Wolf Optimizer based Multi-Objective Quantitative Classification Rules Mining Approaches. Journal of Ambient Intelligence and Humanized Computing. 2021; https://doi.org/10.1007/s12652-020-02701-9.
[15] Akyol S, Alataş B. Plant intelligence based metaheuristic optimization algorithms. Artificial Intelligence Review. 2017; 47:417–45.
[16] Qais MH, Hasanien HM, Alghuwainem S. Identification of electrical parameters for three-diode photovoltaic model using analytical and sunflower optimization algorithm. Applied Energy. 2019; 250: 109-8.
[17] Gomes GF, Almeida FA. Tuning metaheuristic algorithms using mixture design:Application of sunflower optimization for structural damage identification. Advances in Engineering Software. 2020; 149: 102877.
[18] Yuan Z, Wang W, Wang H, Razmjooy N. A new technique for optimal estimation of the circuit-based PEMFCs using developed Sunflower Optimization Algorithm. Energy Reports. 2020; 6: 662-9.
[19] Hussien AM, Hasanien HM, Mekhamer SF. Sunflower optimization algorithm-based optimal PI control for enhancing the performance of an autonomous operation of a microgrid. Ain Shams Engineering Journal. 2021; 12(2):1883-10.
[20] Shaheen MAM, Hasanien HM, Mekhamer SF, Talaat HEA. Optimal Power of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm. IEEE Access. 2019; 7: 109289-11.
[21] Alshammari BM, Guesmi T. New Chaotic Sunflower Optimization Algorithm for Optimal Tuning of Power System Stabilizers. Journal of Electrical Engineering & Technology. 2020; 15: 1985-12.
[22] Gomes GF, Cunha Jr SS, Ancelotti Jr AC. A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Engineering with Computers. 2019; 35: 619-7.
[23] Proença HM, Leeuwen M. Interpretable multiclass classification by MDL-based rule lists. Information Sciences. 2020; 512: 1372-21.
[24] Miranda TZ, Sardinha DB, Cerri R. (2019). Preventing the Generation of Inconsistent Sets of Classification Rules. Expert Systems with Applications. 2019;165.
[25] He C, Ma M, Wang P. Extract Interpretability-Accuracy balanced Rules from Artificial Neural Networks: A Review. Neurocomputing. 2020; 387:346-12.
[26] Kiziloluk S, Alatas B. Automatic mining of numerical classification rules with parliamentary optimization algorithm. Advances in Electrical and Computer Engineering. 2015; 15(4):17-8.