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WOA-Miner: Classification Rule Discovery Using Whale Optimization Algorithm

Year 2019, Volume: 40 Issue: 1, 186 - 196, 22.03.2019

Ufuk Çelik

https://doi.org/10.17776/csj.522039
Cited By: 1

Abstract

This paper proposes a rule discovery tool for
classification by using whale optimization algorithm that simulates the
foraging behavior of humpback whales. Rule extraction is based on the
optimization of randomly selected attributes according to rule fitness value.
Algorithm were implemented and tested the most known 13 datasets and the
results were compared with other known data mining algorithms including
Decision Tree, Naïve Bayes, J48, JRip, Artificial Bee Colony and Ant Colony
Optimization. The obtained results showed that whale optimization algorithm
proved an appropriate candidate for classification processes.

Keywords

Data mining Classification Rule discovery Whale optimization algorithm Whale optimization algorithm

References

  • [1]. Cano A., Zafra A. and Ventura S., An Interpretable Classification Rule Mining Algorithm, Inf Sci (Ny), 240 (2013) 1–20.
  • [2]. Wang J.L. and Chan S.H., Stock Market Trading Rule Discovery Using Two-Layer Bias Decision Tree, Expert Syst Appl, 30-4 (2006) 605–611.
  • [3]. Shukla A., Tiwari R., Ranjan R.A. and Kala R., Multilingual Character Recognition Using Hierarchical Rule Based Classification and Artificial Neural Network, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 5552-2 (2009) 821–830.
  • [4]. Sharawi M., Zawbaa H.M. and Emary E., Feature Selection Approach Based on Whale Optimization Algorithm, Ninth Int. Conf. Adv. Comput. Intell., (2017) 163–168.
  • [5]. Canayaz M. and Demir M., Feature Selection with the Whale Optimization Algorithm and Artificial Neural Network, Int. Artif. Intell. Data Process. Symp., IEEE, (2017) 1–5.
  • [6]. Khan S.A., Nazir M. and Riaz N., Optimized Features Selection for Gender Classification Using Optimization Algorithms, Turkish J Electr Eng Comput Sci, 21-5 (2013) 1479–1494.
  • [7]. Rodrigues D., Papa J.P. and Adeli H., Meta-Heuristic Multi and Many Objective Optimization Techniques for Solution of Machine Learning Problems, Expert Syst, 34-6 (2017) e12255.
  • [8]. Diao R. and Shen Q., Nature Inspired Feature Selection Meta-Heuristics, Artif Intell Rev 44-3 (2015) 311–340.
  • [9]. Celik M., Karaboga D. and Koylu F., Artificial Bee Colony Data Miner (ABC-Miner), Int. Symp. Innov. Intell. Sys. App. (INISTA), (2011) 96–100.
  • [10]. Michelakos I., Mallios N., Papageorgiou E. and Vassilakopoulos M., Ant Colony Optimization and Data Mining, Next Gener. Data Technol. Collect. Comput. Intell., N. Bessis and F. Xhafa, (Eds). Berlin: Springer Heidelberg, 352 (2011) 31–60.
  • [11]. Parpinelli R.S., Lopes H.S. and Freitas A.A., Data Mining with an Ant Colony Optimization Algorithm, Evol Comput IEEE Trans, 6-4 (2002) 321–332.
  • [12]. Shunmugapriya P. and Kanmani S., A Hybrid Algorithm Using Ant and Bee Colony Optimization for Feature Selection and Classification (AC-ABC Hybrid), Swarm Evol Comput, 36 (2017) 27–36.
  • [13]. S. Mirjalili and Lewis A., The Whale Optimization Algorithm, Adv Eng Softw, 95 (2016) 51–67.
  • [14]. Wiley D., Ware C., Bocconcelli A., Cholewiak D., Friedlaender A., Thompson M., et al., Underwater Components of Humpback Whale Bubble-Net Feeding Behaviour, Behaviour, 148-5 (2011) 575–602.
  • [15]. Goldbogen J.A., Friedlaender A.S., Calambokidis J., McKenna M.F., Simon M., Nowacek D.P., Integrative Approaches to the Study of Baleen Whale Diving Behavior, Feeding Performance and Foraging Ecology, BioScience, 63-2 (2013) 90–100.
  • [16]. Gribskov M. and Robinson N.L., Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching, Comput Chem, 20-1 (1996) 25–33.
  • [17]. Fawcett T., An Introduction to ROC Analysis, Pattern Recognit Lett, 27-8 (2006) 861–874.
  • [18]. Dheeru D. and Taniskidou E.K., (UCI) Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml. Retrieved November 7, 2018.
  • [19]. Witten I.H., Frank E., Hall M.A. and Pal C.J., (Eds). Appendix B - The WEKA Workbench in Data Mining 4th Edition Practical Machine Learning Tools and Techniques, USA: Morgan Kaufmann, 2017.
  • [20]. Zwitter M. and Soklic M., Breast Cancer Data Set, University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia, 1998, Available at: https://archive.ics.uci.edu/ml/datasets/breast+cancer. Retrieved November 7, 2018
  • [21]. Wolberg W.H. and Mangasarian O.L., Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology, Proc Natl Acad Sci, 87-23 (1990) 9193–9196.
  • [22]. Mangasarian O.L., Street W.N. and Wolberg W.H., Breast Cancer Diagnosis and Prognosis Via Linear Programming, Oper Res, 43-4 (1995) 570–577.
  • [23]. Shapiro A.D., Structured Induction in Expert Systems. Boston: Addison-Wesley Longman Publishing Co, 1987.
  • [24]. Güvenir H.A., Demiröz G. and İlter N., Learning Differential Diagnosis of Erythemato-Squamous Diseases Using Voting Feature Intervals, Artif Intell Med, 13-3 (1998) 147–165.
  • [25]. Zwitter M. and Soklic M., Lymphography Data Set, University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia, 1988, Available at: http://archive.ics.uci.edu/ml/datasets/Lymphography. Retrieved November 7, 2018.
  • [26]. Schlimmer J., Mushroom Data Set, National Audubon Society Field Guide to North American Mushrooms. USA: National Audubon Society, 1988.
  • [27]. Zupan B., Bohanec M., Bratko I. and Demsar J., Machine Learning by Function Decomposition, ICML, (1997) 421–429.
  • [28]. Olave M., Rajkovic V. and Bohanec M., Chapter 10: An Application for Admission in Public School Systems, Expert Systems in Public Administration, USA: Elsevier Science Ltd., 1989; pp 145–160.
  • [29]. Michatski R.S., Michalski R.S. and Chilausky R.L., Knowledge Acquisition by Encoding Expert Rules Versus Computer Induction’ From Examples: A Case Study Involving Soybean Pathology, 51-1 (2002) 63–67.
  • [30]. Noordewier M.O., Towell G.G. and Shavlik J.W., Training knowledge-based neural networks to recognize genes in DNA sequences, NIPS'90 Proceedings of the 3rd International Conference on Neural Information Processing Systems, San Francisco: Morgan Kaufmann Publishers Inc., 1990; pp 530–536.
  • [31]. Aha D.W., Tic-Tac-Toe Endgame, UCI Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame. Retrieved November 7, 2018.
  • [32]. Schlimmer J., Congressional Voting Records Data Set, 98th Congress, 2nd session 1984, Washington, D.C.: Volume XL: Congressional Quarterly Inc., 1985.
  • [33]. Forsyth R., Zoo Data Set, UCI Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml/datasets/Zoo. Retrieved November 7, 2018.

Balina Optimizasyonu Algoritması Kullanarak Sınıflandırma Kuralları Keşfi: WOA-Madenci

Year 2019, Volume: 40 Issue: 1, 186 - 196, 22.03.2019

Ufuk Çelik

https://doi.org/10.17776/csj.522039
Cited By: 1

Abstract

Bu çalışma, kambur balinaların yiyecek arama
davranışını simüle eden balina optimizasyonu algoritmasını kullanarak
sınıflandırma için bir kural bulma aracı önermektedir. Kural çıkarımı, kural
uygunluğuna göre rastgele seçilen niteliklerin optimizasyonuna dayanır. Algoritma
en bilinen 13 veri setini uygulayarak test etmiş ve sonuçlar Karar Ağacı, Naive
Bayes, J48, JRip, Yapay Arı Kolonisi ve Karınca Koloni Optimizasyonu dâhil
diğer bilinen veri madenciliği algoritmalarıyla karşılaştırılmıştır. Elde
edilen sonuçlar balina optimizasyon algoritmasının sınıflandırma süreçleri için
uygun bir aday olduğunu kanıtlamıştır.

Keywords

Veri madenciliği Sınıflandırma Kural keşfi Balina optimizasyonu algoritması

References

  • [1]. Cano A., Zafra A. and Ventura S., An Interpretable Classification Rule Mining Algorithm, Inf Sci (Ny), 240 (2013) 1–20.
  • [2]. Wang J.L. and Chan S.H., Stock Market Trading Rule Discovery Using Two-Layer Bias Decision Tree, Expert Syst Appl, 30-4 (2006) 605–611.
  • [3]. Shukla A., Tiwari R., Ranjan R.A. and Kala R., Multilingual Character Recognition Using Hierarchical Rule Based Classification and Artificial Neural Network, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 5552-2 (2009) 821–830.
  • [4]. Sharawi M., Zawbaa H.M. and Emary E., Feature Selection Approach Based on Whale Optimization Algorithm, Ninth Int. Conf. Adv. Comput. Intell., (2017) 163–168.
  • [5]. Canayaz M. and Demir M., Feature Selection with the Whale Optimization Algorithm and Artificial Neural Network, Int. Artif. Intell. Data Process. Symp., IEEE, (2017) 1–5.
  • [6]. Khan S.A., Nazir M. and Riaz N., Optimized Features Selection for Gender Classification Using Optimization Algorithms, Turkish J Electr Eng Comput Sci, 21-5 (2013) 1479–1494.
  • [7]. Rodrigues D., Papa J.P. and Adeli H., Meta-Heuristic Multi and Many Objective Optimization Techniques for Solution of Machine Learning Problems, Expert Syst, 34-6 (2017) e12255.
  • [8]. Diao R. and Shen Q., Nature Inspired Feature Selection Meta-Heuristics, Artif Intell Rev 44-3 (2015) 311–340.
  • [9]. Celik M., Karaboga D. and Koylu F., Artificial Bee Colony Data Miner (ABC-Miner), Int. Symp. Innov. Intell. Sys. App. (INISTA), (2011) 96–100.
  • [10]. Michelakos I., Mallios N., Papageorgiou E. and Vassilakopoulos M., Ant Colony Optimization and Data Mining, Next Gener. Data Technol. Collect. Comput. Intell., N. Bessis and F. Xhafa, (Eds). Berlin: Springer Heidelberg, 352 (2011) 31–60.
  • [11]. Parpinelli R.S., Lopes H.S. and Freitas A.A., Data Mining with an Ant Colony Optimization Algorithm, Evol Comput IEEE Trans, 6-4 (2002) 321–332.
  • [12]. Shunmugapriya P. and Kanmani S., A Hybrid Algorithm Using Ant and Bee Colony Optimization for Feature Selection and Classification (AC-ABC Hybrid), Swarm Evol Comput, 36 (2017) 27–36.
  • [13]. S. Mirjalili and Lewis A., The Whale Optimization Algorithm, Adv Eng Softw, 95 (2016) 51–67.
  • [14]. Wiley D., Ware C., Bocconcelli A., Cholewiak D., Friedlaender A., Thompson M., et al., Underwater Components of Humpback Whale Bubble-Net Feeding Behaviour, Behaviour, 148-5 (2011) 575–602.
  • [15]. Goldbogen J.A., Friedlaender A.S., Calambokidis J., McKenna M.F., Simon M., Nowacek D.P., Integrative Approaches to the Study of Baleen Whale Diving Behavior, Feeding Performance and Foraging Ecology, BioScience, 63-2 (2013) 90–100.
  • [16]. Gribskov M. and Robinson N.L., Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching, Comput Chem, 20-1 (1996) 25–33.
  • [17]. Fawcett T., An Introduction to ROC Analysis, Pattern Recognit Lett, 27-8 (2006) 861–874.
  • [18]. Dheeru D. and Taniskidou E.K., (UCI) Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml. Retrieved November 7, 2018.
  • [19]. Witten I.H., Frank E., Hall M.A. and Pal C.J., (Eds). Appendix B - The WEKA Workbench in Data Mining 4th Edition Practical Machine Learning Tools and Techniques, USA: Morgan Kaufmann, 2017.
  • [20]. Zwitter M. and Soklic M., Breast Cancer Data Set, University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia, 1998, Available at: https://archive.ics.uci.edu/ml/datasets/breast+cancer. Retrieved November 7, 2018
  • [21]. Wolberg W.H. and Mangasarian O.L., Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology, Proc Natl Acad Sci, 87-23 (1990) 9193–9196.
  • [22]. Mangasarian O.L., Street W.N. and Wolberg W.H., Breast Cancer Diagnosis and Prognosis Via Linear Programming, Oper Res, 43-4 (1995) 570–577.
  • [23]. Shapiro A.D., Structured Induction in Expert Systems. Boston: Addison-Wesley Longman Publishing Co, 1987.
  • [24]. Güvenir H.A., Demiröz G. and İlter N., Learning Differential Diagnosis of Erythemato-Squamous Diseases Using Voting Feature Intervals, Artif Intell Med, 13-3 (1998) 147–165.
  • [25]. Zwitter M. and Soklic M., Lymphography Data Set, University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia, 1988, Available at: http://archive.ics.uci.edu/ml/datasets/Lymphography. Retrieved November 7, 2018.
  • [26]. Schlimmer J., Mushroom Data Set, National Audubon Society Field Guide to North American Mushrooms. USA: National Audubon Society, 1988.
  • [27]. Zupan B., Bohanec M., Bratko I. and Demsar J., Machine Learning by Function Decomposition, ICML, (1997) 421–429.
  • [28]. Olave M., Rajkovic V. and Bohanec M., Chapter 10: An Application for Admission in Public School Systems, Expert Systems in Public Administration, USA: Elsevier Science Ltd., 1989; pp 145–160.
  • [29]. Michatski R.S., Michalski R.S. and Chilausky R.L., Knowledge Acquisition by Encoding Expert Rules Versus Computer Induction’ From Examples: A Case Study Involving Soybean Pathology, 51-1 (2002) 63–67.
  • [30]. Noordewier M.O., Towell G.G. and Shavlik J.W., Training knowledge-based neural networks to recognize genes in DNA sequences, NIPS'90 Proceedings of the 3rd International Conference on Neural Information Processing Systems, San Francisco: Morgan Kaufmann Publishers Inc., 1990; pp 530–536.
  • [31]. Aha D.W., Tic-Tac-Toe Endgame, UCI Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml/datasets/Tic-Tac-Toe+Endgame. Retrieved November 7, 2018.
  • [32]. Schlimmer J., Congressional Voting Records Data Set, 98th Congress, 2nd session 1984, Washington, D.C.: Volume XL: Congressional Quarterly Inc., 1985.
  • [33]. Forsyth R., Zoo Data Set, UCI Machine Learning Repository, Available at: http://archive.ics.uci.edu/ml/datasets/Zoo. Retrieved November 7, 2018.
There are 33 citations in total.

Details

Primary Language English
Journal Section Engineering Sciences
Authors

Ufuk Çelik 0000-0003-3063-6272

Publication Date March 22, 2019
Submission Date February 4, 2019
Acceptance Date February 18, 2019
Published in Issue Year 2019Volume: 40 Issue: 1

Cite

APA Çelik, U. (2019). WOA-Miner: Classification Rule Discovery Using Whale Optimization Algorithm. Cumhuriyet Science Journal, 40(1), 186-196. https://doi.org/10.17776/csj.522039

Cited By

Association Rule Mining Based on Hybrid Whale Optimization Algorithm
International Journal of Data Warehousing and Mining
https://doi.org/10.4018/IJDWM.308817