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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2021 / Cilt: 10 Sayı: 1

Ensemble learning approach for enhancing performance prediction: experimental study for disabled people

Tahmin performansını arttırmak için kolektif öğrenme yaklaşımı: Engelli bireylere yönelik deneysel çalışma

Melda Kokoç(Gazı University)0000-0003-2035-9777
Fatih Kokoç(GAZİ ÜNİVERSİTESİ)0000-0002-0055-9221
Pages
100–114
Publication Date
2021-01-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

When experimental results of single and ensemble models were compared, it was seen that ensemble learning technique combined with two or more algorithms increased predictive performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the case of too many variables, manual classification becomes a time- and labor-intensive process.
📊
Key Findings & Data:Seven different data mining algorithms (support vector machines, artificial neural networks, C5.0, CART, QUEST, CHAID, bayesian networks) have been used in the estimation process.
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Conclusion & Impact:When experimental results of single and ensemble models were compared, it was seen that ensemble learning technique combined with two or more algorithms increased predictive performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:fiziksel ve motor engel · medikal veri · öz bakım

Abstract

In the case of too many variables, manual classification becomes a time- and labor-intensive process. For instance, classifying people with disabilities according to the problems they experience in self-care activities is a time-consuming process for specialist therapists. The aim of this study is to develop a model that can predict the problems experienced by physically and motor disabled individuals in self-care activities in order to reduce time spent by specialist therapist. Seven different data mining algorithms (support vector machines, artificial neural networks, C5.0, CART, QUEST, CHAID, bayesian networks) have been used in the estimation process. These algorithms have been applied individually and by forming different ensemble models. When experimental results of single and ensemble models were compared, it was seen that ensemble learning technique combined with two or more algorithms increased predictive performance.

Özet

Fazla değişken söz konusu olduğunda elle sınıflama yapmak zaman ve emek isteyen bir süreç haline gelmektedir. Böyle bir duruma örnek olan engelli bireylerin öz bakım aktivitelerinde yaşadıkları sorunlara göre sınıflandırılması, uzman terapistler için zaman alıcı bir süreçtir. Bu çalışmanın amacı uzman terapistlere zaman kazandırması açısından fiziksel ve motor engelli bireylerin öz bakım aktivitelerinde yaşadıkları sorunları tahmin edebilecek bir modelin geliştirilmesidir. Tahmin sürecinde yedi farklı (destek vektör makineleri, yapay sinir ağları, C5.0, CART, QUEST, CHAID, bayes ağları) veri madenciliği algoritmasından yararlanılmıştır. Söz konusu algoritmalar, tek olarak ve farklı kolektif modeller oluşturularak uygulanmıştır. Tek ve kolektif olarak uygulanan modellerin deneysel sonuçları kıyaslandığında, iki veya daha fazla algoritmanın birleştirilmesi ile uygulanan kolektif öğrenme tekniğinin tahmin performansını yükselttiği görülmüştür.

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Cite This Article

Kokoç, M., Kokoç, F., *, M. K. (2021). Ensemble learning approach for enhancing performance prediction: experimental study for disabled people. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 10 Sayı: 1, 100–114. https://doi.org/10.28948/ngumuh.693303

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References (58)

  1. World Health Organization, Disabilities, Accessed 20 November 2019. https://www.who.int/topics /disabilities/en/
  2. World Health Organization (WHO). World report on disability, Accessed 12 December 2019. https://www.ncbi.nlm.nih.gov/books/NBK304079/pdf/Bookshelf_NBK304079.pdf
  3. Türkiye İstatistik Kurumu (TÜİK). Nüfus ve konut araştırması, Erişim 31 Ocak 2013. https://tuikweb.tuik.gov.tr/PreHaberBultenleri.do?id=15843
  4. J. Ditterline, D. Banner, T. Oakland, and D. Becton, Adaptive behavior profiles of students with disabilities,. Journal of Applied School Psychology, 24(2), 191-208, 2008. https://doi.org/ 10.1080/15377900802089973
  5. L. Rosenberg, A. Moran and O. Bart, The associations among motor ability, social-communication skills, and participation in daily life activities in children with low-functioning autism spectrum disorder, Journal of Occupational Therapy, Schools, & Early Intervention, 10(2), 137-46, 2017. https://doi.org/ 10.1080/19411243.2017.1304842
  6. E. Björck-Åkesson et al., The International Classification of Functioning, Disability and Health and the version for children and youth as a tool in child habilitation/early childhood intervention–feasibility and usefulness as a common language and frame of reference for practice, Disability and Rehabilitation, 32 (1), 125-38, 2010.
  7. R. J. Simeonsson et al., Applying the International Classification of Functioning, Disability and Health (ICF) to measure childhood disability, Disability and Rehabilitation, 25(11-12), 602-10, 2003. https://doi.org/10.1080/0963828031000137117
  8. T. K. Wu, S. C. Huang, and Y.R. Meng, Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities, Expert Systems with Applications, 34(3), 1846-56, 2008. https://doi.org/10.1016/j.eswa.2007.02.026
  9. T. K. Wu, Y. R. Meng, and S. C. Huang, Application of Artificial Neural Network to the Identification of Students with Learning Disabilities, in IC-AI, pp. 162-168, 2006.
  10. N. Muangnak, W. Pukdee, and T. Hengsanunkun, Classification students with learning disabilities using Naïve Bayes Classifier and Decision Tree, in The 6th International Conference on Networked Computing and Advanced Information Management, pp. 189-192, 2010.
  11. B. B. Ertürk, İşlevsellik, yetiyitimi ve sağlığın uluslararası sınıflandırılması, Erişim 20 Mayıs 2019. http://www.sosyalsiyaset.net/documents/yeti_yitimi_islevsellik.htm
  12. N. Şahin, H. Altun, ve K. Bilge, Özürlü çocuk sağlık kurulu raporlarının değerlendirilmesi, Kocatepe Tıp Dergisi, 15(1), 48-53, 2014.
  13. M. Zarchi, S. F. Bushehri, and M. Dehghanizadeh, SCADI: A standard dataset for self-care problems classification of children with physical and motor disability, International Journal of Medical Informatics, 114, 81-7, 2018. https://doi.org/10.1016/ j.ijmedinf.2018.03.003
  14. J. S. Chou and A. D. Pham Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-63, 2013. https://doi.org/10.1016/j.conbuildmat. 2013.08.078
  15. L. Jena and N. K. Kamila Distributed data mining classification algorithms for prediction of chronic-kidney-disease, Int. J. Emerg. Res. Manag. & Technology, 4(11), 110-8, 2015.
  16. İ. Ertuğrul, A. Organ, ve A. Şavlı, Veri madenciliği uygulamasına ilişkin PAÜ hastanesinde hasta profilinin belirlenmesi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 19(2), 97-103, 2013. https://doi.org/10.5505/pajes.2013.68077
  17. D. A. Adeniyi, Z. Wei and Y. Yongquan Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method, Applied Computing and Informatics, 12(1), 90-108, 2016. https://doi.org/10.1016/j.aci. 2014.10.001
  18. J. R. Quinlan, Discovering rules by induction from large collections of examples, Editör: Donald, M. Expert systems in The Micro Electronic Age, 168–201, Edinburgh, İskoçya, Edinburgh University Press, 1979.
  19. J. R. Quinlan, Induction of decision trees, Machine Learning, 1(1), 81-106, 1986. https://doi.org/10.1007/BF00116251
  20. J. R. Quinlan, Bagging, boosting, and C4. 5, in AAAI/IAAI, 1,725-30, 1996.
  21. J. R. Quinlan, C4.5: Programs for Machine Learning San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., 1993.
  22. V. Figueiredo, F. Rodrigues, Z. Vale, and J. B. Gouveia, An electric energy consumer characterization framework based on data mining techniques, IEEE Transactions on Power Systems, 20(2), 596-602, 2005. https://doi.org/10.1109/TPWRS. 2005.846234
  23. M. B. Kılıçalan, Hanehalkı işgücü araştırma verileri ile veri madenciliği yöntemlerinin uygulanması ve modellerin karşılaştırılması, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Hacettepe Üniversitesi, Ankara, Türkiye, 2018.
  24. B. Lantz, Machine learning with R. Packt Publishing Ltd, Birmingham, İngiltere, 2013.
  25. S. L. Pang and J. Z. Gong, C5. 0 classification algorithm and application on individual credit evaluation of banks, Systems Engineering-Theory & Practice, 29(12), 94-104, 2009. https://doi.org/10.1016/S1874-8651(10)60092-0
  26. L. Breiman, J. H. Friedman and R.A. Olshen, C.J. Stone Classification and regression trees. Belmont, CA: Wadsworth. International Group, 432, 151-66, 1984. https://doi.org/10.1002/widm.8
  27. A. M. Elsayad and H.A. Elsalamony, Diagnosis of breast cancer using decision tree models and SVM, International Journal of Computer Applications, 83(5), 19-29, 2013.
  28. E. Şatir, F. Azboy, A. Aydin, H. Arslan ve Ş. Haciefendioğlu Veri indirgeme ve sınıflandırma teknikleri ile glokom hastalığı teşhisi, El-Cezeri Journal of Science and Engineering, 3(3), 485-97, 2016.
  29. W. Y. Loh and Y. S. Shih, Split selection methods for classification trees, Statistica Sinica, pp. 815-40, 1997.
  30. M. Coşkun and H. İ. Bülbül, Hane halki internet hizmeti sahipliğini etkileyen faktörlerin karar ağaçlari ile incelenmesi. Türk Bilim Araştırma Vakfı, 12(2), 1-17, 2019.
  31. E. Ekici, Farklı sınıflandırma yöntemlerinin karşılaştırılması ve bir uygulama/An application on the comparison of various classification methods, Yüksek Lisans Tezi, Fırat Üniversitesi, Elâzığ, Türkiye, 2012.
  32. G. V. Kass, An exploratory technique for investigating large quantities of categorical data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 29(2), 119-127, 1980. https://doi.org/10.2307/ 2986296
  33. O. F. Althuwaynee, B. Pradhan, and S. Lee, A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison, International Journal of Remote Sensing, 37(5), 1190-209, 2016. https://doi.org/ 10.1080/01431161.2016.1148282
  34. M. Chambers and T. W. Dinsmore, Advanced Analytics Methodologies: Driving Business Value with Analytics, Londra, Pearson Education, 2014.
  35. M. Abdar, M. Zomorodi-Moghadam, R. Das, and I.H. Ting, Performance analysis of classification algorithms on early detection of liver disease, Expert Systems with Applications, 67, 239-51, 2017. https://doi.org/ 10.1016/j.eswa.2016.08.065
  36. V. Vapnik and A. Chervonenkis, Theory of pattern recognition, ed: Nauka, Moscow, 1974.
  37. C. J. Burges A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2(2), 121-67, 1998. https://doi.org/ 10.1023/A:1009715923555
  38. W. S. Noble, What is a support vector machine? Nature Biotechnology, 24(12), 1565-7, 2006. https://doi.org/10.1038/nbt1206-1565
  39. C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20(3), 273-97, 1995. https://doi.org/10.1007/BF00994018
  40. E. Osuna, R. Freund, and F. Girosit, Training support vector machines: an application to face detection, in Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp. 130-6, 1997. https://doi.org/10.1109/CVPR1997.609310
  41. C. C. Yeh, D. J. Chi, and M. F. Hsu, A hybrid approach of DEA, rough set and support vector machines for business failure prediction, Expert Systems with Applications, 37(2), 1535-41, 2010. https://doi.org/10.1016/j.eswa.2009.06.088
  42. C. W. Hsu, C. C. Chang, and C. J. Lin, A practical guide to support vector classification, ed: Taipei, 2003.
  43. W. S. McCulloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys, 5(4), 115-33, 1943. https://doi.org/10.1007/BF02478259
  44. F. Rosenblatt, The perceptron, a perceiving and recognizing automaton Project Para, Cornell Aeronautical Laboratory, 1957.
  45. H. Erdal, Makine öğrenmesi yöntemlerinin inşaat sektörüne katkısı: basınç dayanımı tahminlemesi, Pamukkale University Journal of Engineering Sciences, 21(3), 109-14, 2015. https://doi.org/ 10.5505/pajes.2014.26121
  46. E. Aydemir, M. Karaatlı, G. Yılmaz, ve S. Aksoy, 112 Acil çağrı merkezine gelen çağrı sayılarını belirleyebilmek için bir yapay sinir ağları tahminleme modeli geliştirilmesi, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 20(5), 145-9, 2014. https://doi.org/10.5505/pajes.2014.98608
  47. Ş. Haciefendioğlu, Makine öğrenmesi yöntemleri ile glokom hastalığının teşhisi, Yüksek Lisans Tezi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Konya, 2012.
  48. H. B. Kartal, Envanter sınıflandırmada yapay öğrenme yöntemlerinin kullanımı ve destek vektör makineleri ile bir uygulama, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, İstanbul Teknik Üniversitesi, İstanbul, 2012.
  49. M. Karabıyık ve B. Yet, Bayes ağları ile futbol analitiği: FutBA modeli, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(1), 121-31, 2019.
  50. E. N. Çinicioğlu, M. Atalay, ve H. Yorulmaz, Trafik kazaları analizi için bayes ağları modeli, Bilişim Teknolojileri Dergisi, 6(2), 41, 2013.
  51. J. M. Moyano, E. L. Gibaja, K. J. Cios, and S. Ventura, Review of ensembles of multi-label classifiers: models, experimental study and prospects, Information Fusion, 44, 33-45, 2018. https://doi.org/10.1016/ j.inffus.2017.12.001
  52. T. G. Dietterich, Ensemble methods in machine learning, in International Workshop On Multiple Classifier Systems, 2000, Springer. https://doi.org/ 10.1007/3-540-45014-9_1
  53. UCI Machine Learning Repository. SCADI Data Set, Accessed: 06 December 2018. https://archive.ics.uci.edu/ml/datasets/SCADI
  54. I. A. Gheyas and L. S. Smith, Feature subset selection in large dimensionality domains, Pattern Recognition, 43(1), 5-13, 2010. https://doi.org/10.1016/ j.patcog.2009.06.009
  55. S. Gu, R. Cheng, and Y. Jin, Feature selection for high-dimensional classification using a competitive swarm optimizer, Soft Computing, 22(3), 811-22, 2018. https://doi.org/10.1007/s00500-016-2385-6
  56. M. K. Keleş and Ü. Kılıç, Artificial bee colony algorithm for feature selection on scadi dataset, in 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 463-6, 2018. https://doi.org/10.1109/UBMK.2018.8566287
  57. H. B. F. David, H. B. F. and A. Suruliandi, Performance evaluation of ensemble classifiers on benchmark datasets, Journal of Emerging Technologies and Innovative Research, 6(2), 926-30, 2019.
  58. A. Choudhury, Classification of functioning, disability, and health: ICF-CY self care (SCADI dataset) using predictive analytics, in Choudhury A., Classification of Functioning, Disability, and Health: ICF-CY Self Care.(SCADI DATASET) Using Predictive Analytics. In: Proceedings of the 2019 IISE Annual Conference. Edited by HE Romeijn AS, R. Thomas. Orlando: IISE, 2019. http://dx.doi.org/10.2139/ssrn.3307719