|تعداد مشاهده مقاله||2,480,996|
|تعداد دریافت فایل اصل مقاله||1,748,428|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقاله 6، دوره 10، شماره 1، فروردین 2022، صفحه 57-74 اصل مقاله (849.82 K)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2021.7856.441|
|H. Nosrati Nahook* ؛ S. Tabatabaei|
|Department of Computer Engineering, Faculty of Engineering, Higher Education Complex of Saravan, Saravan, Iran.|
|تاریخ دریافت: 12 دی 1399، تاریخ بازنگری: 29 اسفند 1399، تاریخ پذیرش: 09 اردیبهشت 1400|
|Background and Objectives: The Ant-Miner algorithm works based on Ant Colony Optimization as a tool for data analysis , and is used to explore classified laws from a set of data. In the current study, two new methods have been proposed for the purpose of optimizing this algorithm. The first method adopted logical negation operation on the records of the produced laws, while the second employed a new Pheromone Update strategy called “Generalized exacerbation of quality conflict”. The two proposed methods were executed in Visual studio C#.Net , and 8 public datasets were applied in the test. Each one of these datasets was executed 10 times both in an independent way and combined with others, and the average results were recorded.|
Methods: In this study, we have proposed two approaches for the earlier method. Using the first method in the construction of rule records, idioms that include the rules can be made in the form of . Compared to the idioms of the early algorithm, these idioms are more compatible while constructing rules with high coverage. The advantage of this generalization is the reduction of the produced rules, which results in greater understandability of the output. During the process of pheromone update in the ordinary ACO algorithms, the amount of the sprayed pheromone is a function of the quality of rules. The objective of the second method is to strengthen the conflict between not-found, weak, good, and superior solutions. This method is a new strategy of pheromone update where ants with high-quality solutions are motivated through increasing the amount of pheromone sprayed on the trail that they have found; conversely, the ants that find weaker solutions are punished through eliminating pheromone from their trails.
Results: The optimization of the initial algorithm using the two proposed methods produces a smaller number of rules, but increases the number of construction diagrams and prevents the production of low-quality rules.
Conclusion: The results of tests performed on the dataset indicated the enhancement of algorithm efficiency in idioms of fewer tests, increased prediction accuracy of laws, and improved comprehensibility of the produced laws using the proposed methods.
|Qualitative؛ contrast؛ intensifier؛ Logical؛ negation|
 A. Abraham, C. Grosan, M. Chis, Swarm Intelligence in Data Analysis, Studies in Computational Intelligence, vol. 34, Springer: 1-20, 2006.
 M. Dorigo, A. Colorni, V. Maniezzo, “The ant system: optimization through a colony of cooperating agents,” IEEE Trans. Syst. Man Cybern. Part B Cybern., 26: 29–41, 1996.
 S. Tabatabaei, H. Nosrati Nahook, “A new routing protocol in MANET using Cuckoo optimization algorithm,” J. Electr. Comput. Eng. Innovations, 9(1): 75-82, 2021.
 M. Medland, F.E.B Otero, A.A. Freitas, "Improving the cAnt-Miner PB classification algorithm," presented at the International Conference on Swarm Intelligence. Springer, Berlin, Heidelberg, 2012.
 M.G.H. OMRAN, S. AL-SHARHAN, “Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems,” Eng. Appl. Artif. Intell., 85: 818-829, 2019.
 R.S. Parpinelli, H.S. Lopes, A.A. Freitas, “Data mining with an ant colony optimization algorithm,” IEEE Trans. Evol. Comput., 6(4): 321-332, 2002.
 W.J. Jiang, Y.H. Xu, Y.S. Xu, "A novel data mining algorithm based on ant colony system," in Proc. 2005 International Conference on Machine Learning and Cybernetics, 3: 1919-1923. 2005.
 B. Liu, H.A. Abbas, B. McKay, "Classification rule discovery with ant colony optimization," in Proc. IEEE/WIC International Conference on Intelligent Agent Technology (IAT): 83-88, 2003.
 J. He, D. Long, C. Chen, "An improved ant-based classifier for intrusion detection," in Proc. Third International Conference on Natural Computation (ICNC 2007), 4: 819-823, 2007.
 A. Chan, A. Freitas, "A new classification-rule pruning procedure for an ant colony algorithm," in Proc. International Conference on Artificial Evolution (Evolution Artificielle): 25-36, Springer, Berlin, Heidelberg, 2005.
 K. Thangavel, P. Jaganathan, "Rule mining algorithm with a new ant colony optimization algorithm," in Proc. International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), 2: 135-140, 2007.
 D. Martens, M. D. Backer, R. Haesen, J. Vanthienen, M. Snoeck, B. Baesens, "Classification with ant colony optimization," IEEE Trans. Evol. Comput., 11(5): 651-665, 2007.
 Y. Chen, L. Chen, L.Tu, "Parallel ant colony algorithm for mining classification rules," in Proc. 2006 IEEE International Conference on Granular Computing: 85-90, 2006.
 B. Jyoti, A.K. Sharma, "AntMiner: Bridging the gap between data mining classification rule discovery and bio-inspired algorithms," in Proc. International Conference on IoT Inclusive Life (ICIIL 2019), NITTTR Chandigarh, India: 269-277, 2020.
 B.K. Nanda, S. Dehuri, "Ant Miner: A hybrid pittsburgh style classification rule mining algorithm," Int. J. Artif. Intell. Mach. Learn. (IJAIML), 10(1): 45-59, 2020.
 A. Singh, Akansha, G. Gupta, "ANT_FDCSM: A novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients," J. Intell. Fuzzy Syst. 36(1): 747-760, 2019.
 M. Durgadevi, R. Kalpana, "Medical distress prediction based on classification rule discovery using ant-miner algorithm," in Proc. 2017 11th International Conference on Intelligent Systems and Control (ISCO): 88-92, 2017.
 H.N.K. Al-Behadili, K.R. Ku-Mahamud, R. Sagban, "Ant colony optimization algorithm for rule-based classification: Issues and potential solutions," J. Theor. Appl. Inf. Technol., 96(21): 7139-7150, 2018.
 J. Kozak, Decision Tree and Ensemble Learning According to Ant Colony Optimization. Springer, Cham: 29-44, 2019.
 L. Wan, C. Du, “An approach to evaluation of environmental benefits for ecological mining areas according to ant Colony algorithm,” Earth Sci. Inf., 14: 797–808, 2021.
 B. Shuang, J. Chen, Z. Li, “Study on hybrid PS-ACO algorithm,” Appl. Intell., 34(1): 64-73, 2011.
 R.S. Parpinelli, A. Freitas, “Data analysis with an ant colony optimization algorithm,” IEEE Trans. Evol. Comput., 6: 321–332, 2002 .
 B. Liu, H. Abbass, B. McKay, “Density-Based exploratory for rule discovery with Ant-Miner,” in Proc. 6th Australasia-Japan Joint Workshop on Intell. Evol. Syst.: 180–184, 2002.
 B. Liu, H. Abbass, B. McKay, “Categorizing rule discovery with ant colony optimization,” in Proc. IEEE/WIC Int. Conf. Intell. Agent Technology: 83–88, 2003.
 A. Chan, A. Freitas, “A new classification-rule pruning procedure for an ant colony algorithm, Artificial Evolution,” Lect. Notes Comput. Sci., 3871: 25–36, 2005.
 A. Chan, A., Freitas, “A new ant colony algorithm for multi-label grouping with applications in bioinformatics,” in Proc. Genetic and Evolutionary Computation Conf.: 27-34, 2006.
 D. Martens et al., “Categorizing with ant colony optimization,” IEEE Transactions on Evolutionary Computation. vol. 11, pp. 651–665, 2007.
 T. Stützle, M. Dorigo, “ACO algorithms for the traveling salesman problem,” Evolutionary Algorithms in Engineering and Computer Science, Wiley: 163-183, 1999.
 H.N.K. Al-Behadili, K.R. Ku-Mahamud, R. Sagban, “Rule pruning techniques in the ant-miner classification algorithm and its variants: A review,” in Proc. 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE): 78-84, 2018.
 U. Ayub, H. Naveed, W. Shahzad, “PRRAT_AM—An advanced ant-miner to extract accurate and comprehensible classification rules,” Appl. Soft Comput.: 106326, 2020.
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