Cilt 6 Sayı 3 (2018): BUSINESS & MANAGEMENT STUDIES: AN INTERNATIONAL JOURNAL
Makaleler

ÜLKELERİN İNSANİ GELİŞMİŞLİK ENDEKSİNE GÖRE YENİDEN SINIFLANDIRILMASI: YAPAY SİNİR AĞI VE ANFIS YÖNTEMLERİ İLE BİR UYGULAMA

PDF (English)
  • Ömer Faruk RENÇBER,
  • Sinan METE
Ömer Faruk RENÇBER
Çukurova Üniversitesi
Sinan METE
Aksaray Üniversitesi
DOI: https://doi.org/10.15295/bmij.v6i3.356

Yayınlanmış 2018-11-29

Anahtar Kelimeler

  • İnsani Gelişmişlik Endeksi,
  • Yapay Sinir Ağı,
  • Sınıflandırma,
  • ANFIS

Nasıl Atıf Yapılır

RENÇBER, Ömer F., & METE, S. (2018). ÜLKELERİN İNSANİ GELİŞMİŞLİK ENDEKSİNE GÖRE YENİDEN SINIFLANDIRILMASI: YAPAY SİNİR AĞI VE ANFIS YÖNTEMLERİ İLE BİR UYGULAMA. Business & Management Studies: An International Journal, 6(3), 228–252. https://doi.org/10.15295/bmij.v6i3.356

Özet

İstatistik, ekonometri ve veri madenciliği alanlarında sınıflandırma problemlerine sıklıkla karşılaşılmaktadır. Bu amaç doğrultusunda kullanılan yöntemler teknolojiye bağlı olarak günden güne değişmekte ve gelişmektedir. Bu kapsamda çok değişkenli istatistik ve yapay zeka yöntemleri günümüzde kullanılmaktadır. Bu çalışmada, makine öğrenme tekniklerinden yapay sinir ağı (ANN) ve YSA ile bulanık mantık tekniğinin birleşimi olan ve hibrid öğrenme tekniğine dayanan Adaptif Ağ Tabanlı Bulanık Çıkarım Sistemi (Adaptive Neural Fuzzy Inference System-ANFIS) yöntemlerinin sınıflandırma performanslarının karşılaştırılması amaçlanmaktadır. Bu amaç doğrultusunda Birleşmiş Milletler Dünya Gelişmişlik Göstergeleri ve ANN ve ANFIS yöntemleri kullanılarak İnsani Gelişmişlik Endeksi’ne (HDI) göre ülkeler sınıflandırılmış ve elde edilen sonuçlar İGE ile karşılaştırılmıştır. Analiz sonuçları ele alındığında, iktisadi açıdan; çalışmada hesaplanan tahmini endekse göre gelişmişlik, İGE’den farklı olarak, yedi faktör ve sekiz ana konudan oluşmaktadır. İstatistiki açıdan ülkeler; ANN’ye göre %87.5 ve ANFIS’e göre %91.36 oranında doğru sınıflandırılmıştır. Bu durumda ANFIS yönteminin ANN’den daha başarılı sonuçlar verdiği gözlenmiştir.

PDF (English)

İndirmeler

İndirme verileri henüz mevcut değil.

Referanslar

  1. Agrawal N. & Agrawal, J. (2015). “Neural Network Techniques for Cancer Prediction: A Survey”. 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, (s. 769-774).
  2. Akder, A. H. (1994). “A Means to Closing Gaps: Disaggregated Human Development Index”. New York: Human Development Repon Office Occasional Paper 18.
  3. Akkoç S. (2012). “An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data”, European Journal of Operational Research, 222(1), 168-178. doi:10.1016/j.ejor.2012.04.009
  4. Alfonso, C., Sassi, R. & Barreiros, R. (2015). “Biological Image Classification Using Rough Fuzzy Artificial Neural Network”. Expert Systems and Applications, 9482-9488.
  5. Alkire S., ve Foster, J. (2010). “Designing the Inequality-Adjusted Human Development Index (HDI)”. Oxford Poverty and Human Development Initiative, 3-27.
  6. Aşıcı, A. A. (2012). “İktisadi Düşüncede Çevrenin Yeri ve Yeşil Ekonomi: Karşılaştırmalı Bir Analiz”. Yeşil Ekonomi, 35-45.
  7. Atashi, A., Nazeri, N., Abbasi, E., Dorri, S., & Alijani-Z, M. (2017), “Breast Cancer Risk Assessment Using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Subtractive Clustering Algorithm”. Multidisciplinary Cancer Investigation, 1(2), 20- 26.
  8. Aydın, A. & Cavdar S. (2015). “Comparison of Prediction Performances of Artificial Neural Network (ANN) and Vector Autoregressive (VAR) Models by Using the Macroeconomic Variables of Gold Prices, Borsa Istanbul (BIST) 100 Index and US Dollar-Turkish Lira (USD/TRY) Exchange Rates”, 4. Economics ve Finance Conference, (s. 25-28).
  9. Badea, L. M. (2014). “Predicting consumer behavior with artificial neural networks”. Precedia Economics and Finance, 238-246.
  10. Bagheri, A., Mohammadi Peyhani, H. and Akbari, M. (2014). “Financial forecasting using ANFIS networks with Quantum-behaved Particle Swarm Optimization. Expert Systems with Applications”, 41(14), 6235-6250. doi:10.1016/j.eswa.2014.04.003
  11. Bataineh, M., Marler, T., Malek, K. & Arora, J. (2016). “Neural Network for Dynamic Human Motion Prediction”. Expert Systems with Applications, 26-34.
  12. Baykal, N. & Beyan, T. (2004). “Bulanık Mantık İlke ve Temelleri” Ankara: Bıçaklar Kitabevi.
  13. Bhardwaj, A., Tiwari, A., Bhardwaj, H. & Bhardwaj, A. (2016). “A Genetically Optimized Neural Network Model For Multiclass Classification”. Expert Systems with Applications, 211-221.
  14. Bourquin, J., Schmidli, H., Hoogevest, P. & Leuenberger, H. (1998). “Advantages of Artificial Neural Networks (ANNs) as alternative modelling technique for data sets Showing Non-Linear Relationships Using Data From A Galenical Study On A Solid Dosage Form”. Europian Journal of Pharmaceutical Sciences, 5-16.
  15. Boyacioglu, M. A. & Avci, D. (2010).”An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange”. Expert Systems with Applications, 37(12), 7908-7912. doi:10.1016/j.eswa.2010.04.045
  16. Bravo, G. (2014). “The Human Sustainable Development Index: New calculations and a first critical analysis”. Ecological Indicators, 145-150.
  17. Briesch, R. & Rajagopal, P. (2010). “Neural Network Applications in Consumer Behavior”. Journal of Consumer Psyhology, 381-389.
  18. Bukharov, O. & Bogolyubov, D. (2015). “Development of a Decision Support System Based on Neural Networks and a Genetic Algotihm”. Expert System with Applications, 6177-6183.
  19. Burmaoğlu S., Oktay, E. & Üstün, Ö. (2009). “Birleşmiş Milletler Kalkınma Programı Beşeri Kalkınma Endeksi Verilerini Kullanarak Diskriminant Analizi ve Lojistik Regresyon Analizinin Sınıflandırma Performanslarının Karşılaştırılması”. Savunma Bilimleri Dergisi, 23-49.
  20. Çetin, M. Ö.& Işıl, B. (2009). “Dünyada ve Türkiye'de Yoksulluk ve Kadinlar”. Journal of Yasar University, 2661-2698.
  21. Chakravarty R. (2003). “A Generalized Human Development Index”. Review of Development Economics, 7(1), 99-114.
  22. Chandra, B. & Babu, K. (2014). “Classification Of Gene Expression Data Using Spiking Wavelet Radial Basis Neural Network”. Expert Systems with Applications, 1326-1330.
  23. Chang, H. H., Larson, J., Spong Y., C., Howson P., C., Smith:-C., Lackritz, E., . . . E Lawn, J. (2013). “Preventing preterm births: analysis of trends and potential reductions with interventions in 39 countries with very high human development index”. The Lancet 223-234.
  24. Chang, T. S. (2011). “A Comparative Study of Artificial Neural Networks, and Decision Trees for Digital Game Content Stocks Price Prediction”. Expert Systems with Applications, 14846-14851.
  25. Charmes, J. & Wieringa S. (2003). “Measuring Women's Empowerment: An assessment of the Gender-related Development Index and the Gender Empowerment Measure.” Journal of Human Development, 419-436.
  26. Chen, M.-Y. (2013). “A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering”. Information Sciences, 220, 180-195. doi:10.1016/j.ins.2011.09.013
  27. Chien C., Wang, T.-Y. & Lin:-L. (2009). “Application of neuro-fuzzy networks to forecast innovation performance – The example of Taiwanese manufacturing industry”. Expert Systems with Applications, 37(2), 1086-1095. doi:10.1016/j.eswa.2009.06.107
  28. Çiftçi, M. (2008). “Kalkınma Göstergesi Olarak Ortalama Yaşam Beklentisine Göre Türkiye'nin AB İçindeki Konumu: Kritikler ve Çok Değişkenli İstatistik Uygulamaları.” İstanbul Üniversitesi İktisat Fakültesi Ekonometri ve İstatistik Dergisi, 51-87.
  29. Çivi, E., Erol, İ., İnanlı, T.& Erol, E. D. (2008). “Uluslararası Rekabet Gücüne Dayalı Farklı Bakışlar”. Ekonomik ve Sosyal Araştırmalar Dergisi, 1-22.
  30. Coşkun S. Özgenç, N. & Güneş S. (2015). “Sosyal Performansın Ölçümünde Yeni Yöntem: Sosyal Gelişme Endeksi ve Türkiye'nin Görünümü”. Sosyal Politika Çalışmaları Dergisi, 121-153.
  31. Costea, A. (2014). “Applying Fuzzy Logic And Machine Learning Techniques in Financial Performance Predictions”, Procedia Economic and Finance, 4-9.
  32. Crowdhury S. & Squire, L. (2006). “Setting weights for aggregate indices: An application to the commitment to development index and human development index”. The Journal of Development Studies, 761-777.
  33. Dawson, E. & Wilby, R. (1998). “An Artificial Neural Network Approach to Rainfall Runoff Modelling”. Hydrological Sciences Journal, 47-66.
  34. Deb: (2015). “The Human Development Index and Its Methodological Refinements”. Sage Publication, 131-137.
  35. Despotis, D. (2005). “A reassessment of the human development index via data envelopment analysis”. Journal of the Operational Research Society, 969-980.
  36. Do, Q. H. & Chen, J. (2013). “A neuro-fuzzy approach in the classification of students”. Computational intelligence and neuroscience, 1-21.
  37. Doğancı, Ö., Ertürk, M., Özsunar, A.& Arcaklıoğlu, E. (2016). “Orta Ve Batı Karadeniz Bölgesi Rüzgâr Enerjisi Tahmin Çalışması.” İleri Teknoloji Bilimleri Dergisi.
  38. Donel, B. (2012). “Yapay Sinir Ağları ile Kredi Skorlama”. İstanbul: İstanbul Üniversitesi Fen Bilimleri Enstitüsü Yayımlanmamış Doktora Tezi.
  39. Dreiseitl S. & Machado, L. (2002). “Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review”. Journal of Biomedical Informatics, 352-359.
  40. Efendigil, T., Önüt S & Kahraman, C. (2009). “A Decision Support System for Demand Forecasting with Artificial Neural Networks and Neuro Fuzzy Models A Comperative Analysis”. Expert Systems with Applications, 6697-6707.
  41. Elmas, Ç. (2003). “Yapay Sinir Ağları”, Ankara: Seçkin Yayıncılık
  42. El-Sebakhy, E. A. (2009). “Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems”. Computers ve Geosciences, 35(9), 1817-1826. doi:10.1016/j.cageo.2007.10.016
  43. Falat, L., Stanikova, Z., Durisova, M., Holkova, B. & Pokanova, T. (2015). “Application of Neural Network Models in Modelling Economic Time Series with Non-constant Volatility”. Business Economics and Management 2015 Conference, BEM2015, (s. 600-607).
  44. Fish, K., Barnes, J. & Aiken, M. (1995). “Artificial Neural Networks: A New Methodology for Industrial Market Segmentation”. Industrial Marketing Management, 431-439.
  45. Gallo, C., Conto, F., Sala, P. L. & Antonazzo, A. P. (2013). “A Neural Network Model for Classifying Olive Farms”. Procedia Technology, 593-599.
  46. Garrido, C., Ona, R. & Ona, J. (2014). “Neural Networks For Analyzing Service Quality in Public Transportation”. Expert System and Applications, 6830-6838.
  47. Gelmereanu, C., Morar, L. & Bogdan: (2014). “Productivity and Cycle Time Prediction Using Artificial Neural Network,” Procedia Economics and Finance, 1563-1569.
  48. Güneri, A. F., Ertay, T. & Yücel, A. (2011). “An approach based on ANFIS input selection and modeling for supplier selection problem”. Expert Systems with Applications, 38(12), 14907-14917. doi:10.1016/j.eswa.2011.05.056
  49. Haga, J., Siekkinen, J. & Sundvik, D. (2015). “A Neural Network Approach to Measure Real Activities Manipulation”. Expert Systems with Applications, 2313-232.
  50. Harttgen, K. & Klasen S (2011). “A Household-Based Human Development Index.” World Development, 878-899.
  51. Hicks, D. A. (1997). “The Inequality-Adjusted Human Development Index: A Constructive Proposal”. World Development, 1283-1298.
  52. Ho, Y.-C. & Tsai, C.-T. (2011). “Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance”. Expert Systems with Applications, 38(6), 6498-6507. doi:10.1016/j.eswa.2010.11.095
  53. Hsu, K. T. (2011). “Using a Back Propogation Network Combined with Grey Clustering to Forecast Policyholder Decision to Purchase Investent Insurance”. Expert Systems with Applications, 6736-6747.
  54. Iphar, M. (2012). “ANN and ANFIS performance prediction models for hydraulic impact hammers”. Tunnelling and Underground Space Technology, 27(1), 23-29. doi:10.1016/j.tust.2011.06.004
  55. Jang, J. S. (1993). “ANFIS: Adaptive-Network-Based Fuzzy Inference System”. IEEE Transactions on Systems, 665-685.
  56. Joo S, Oh S, Sim, T., Kim, H., Choi, C. & Koo, H. (2014). “Prediction of Gait Speed from Plantar Pressure Using Artificial Neural Networks”. Expert Systems with Applications, 7398-7405.
  57. Karadeniz, O. (2012). “Dünya’da ve Türkiye’de İş Kazaları ve Meslek Hastalıkları ve Sosyal Koruma Yetersizliği”. Çalışma ve Toplum, 15-72.
  58. Karataş, M.& Çankaya, E. (2010). “İktisadi Kalkınma Sürecinde Beşeri Sermayeye İlişkin Bir İnceleme”. Mehmet Akif Ersoy Sosyal Bilimler Enstitüsü Dergisi, 29-55.
  59. Kaya, M. O., Çolak, C.& Özdemir, E. (2013). “Prostat Spesifik Antijeni Yardımı ile Prostat Kanserinin Değişik Yapay Sinir Ağı Modelleri ile Tahmini”. İnönü Üniversitesi Sağlık Bilimleri Dergisi, 19-22.
  60. Khashei, M., Hamadani, A. & Bijari , M. (2012). “A Novel Hybrid Classification Model of Artificial Neural Networks and Multiple Linear Regression Models”. Expert Systems with Applications, 2606-2620.
  61. Koç, A. (2013). “Beşeri Sermaye ve Ekonomik Büyüme İlişkisi: Yatay Kesit Analizi ile AB Ülkeleri Üzerine Bir Değerlendirme”. Maliye Dergisi, 241-285.
  62. Kristjanpoller, W. & Minutolo, M. (2015). “Gold Price Volatility: A Forecasting Approach Using The Artificial Neural Network-GARCH Model”. Expert Systems with Applications, 7245-7251.
  63. Kuyucu, Y. (2012). “Lojistik regresyon analizi (LRA), yapay sinir ağları (YSA) ve sınıflandırma ve regresyon ağaçları (CveRT) yöntemlerinin karşılaştırılması ve tıp alanında bir uygulama”. Ankara: Gazi Üniversitesi Sağlık Bilimleri Enstitüsü.
  64. Kwong, C. K., Wong, T. C. & Chan, K. Y. (2009). “A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach”. Expert Systems with Applications, 36(8), 11262-11270. doi:10.1016/j.eswa.2009.02.094
  65. Lee S & Choeh, J. (2014). “Predicting the Helpfulness of Online Reviews Using Multilayer Perceptron Neural Networks”. Expert Systems with Applications, 3041-3046.
  66. Lee, C. & Yang, O. (2009). “A Neural Networks Approach for Forecasting the Supplier's Bid Prices in Supplier Selection Negotiation Process.” Expert Systems with Applications, 2263-2273.
  67. Lee, K. & Park, J. (1992). “Short Term Load Forecasting Using an Artificial Neural Network”. Transactions on Power Systems, 124-133.
  68. Liao S-H. & Wen, C. (2007). “Artificial Neural Networks Classification and Clustering of Methodologies and Applications-Literature Analysis from 1995 to 2005.” Expert Systems with Applications, 1-11.
  69. Liew, P. Lee, Y. Lin, Y. Lee, T., Lee, W., Wang, W. & Chien, C. (2007). “Comparision of Artificial Neural Networks with Logistic Regression in Prediction of Gallbladder Disease Among Obese Patients”. Digestive and Liver Disease, 356-362.
  70. Mashrei, M. A., Abdulrazzaq, N., Abdalla, T. Y. & Rahman, M. S. (2010). “Neural networks model and adaptive neuro-fuzzy inference system for predicting the moment capacity of ferrocement members”. Engineering Structures, 32(6), 1723-1734. doi:10.1016/j.engstruct.2010.02.024
  71. Mercimek, B., & Çağlayan, Ç. (2017), “İnsani Gelişme, Toplumsal Cinsiyet Eşitsizliği ve Beklenen Yaşam Süresi Üzerine Bir Korelasyon Çalışması”. 19. Halk Sağlığı Kongresi. Antalya.
  72. Moayer S & Bahri, P. (2009). “Hybrid Intelligent Scenario Generator for Business Strategic Planning by Using ANFIS”. Expert Systems with Applications, 7729-7737.
  73. Omiotek, Z., Burda, A. & Wojcik, W. (2013). “The Use Of Decision Tree Induction And Artificial Neural Networks For Automatic Diagnosis Of Hashimoto'S Disease”. Expert Systems with Applications, 6684-6689.
  74. Önder, H. & Şenses, F. (2006). T”ürkiye'de Yoksulluk ve Yoksulluk Düşüncesi. İktisat, Siyaset, Devlet Üzerine Yazılar”, 199-221.
  75. Ottenbacher, K., Linn, R., Smith, P., Illig S, Mancuso, M. & Granger, C. (2004). “Comparison Of Logistic Regression And Neural Network Analysis Applied To Predicting Living Setting After Hip Fracture”. Annals of Epidemiology, 551-559.
  76. Ottenbacher, K., Smith, P., Ilig S, Linn, R., Fiedler, R. & Granger, C. (2001). “Comparison Of Logistic Regression And Neural Networks To Predict Rehospitalization in Patients With Stroke”. Journal of Clinical Epidemiology, 1159-1165.
  77. Özçalıcı, M. (2017), “Aşırı Öğrenme Makineleri İle Hisse Senedi Fiyat Tahmini.” Hacettepe University Journal of Economics and Administrative Sciences, 35(1), 67-88.
  78. Özcan, İ., Şahin, A., Dikmen, E.ve Bayram, G. (2013). “Isparta İlinde Rüzgâr Hızı Değerlerinin Belirlenmesi”. SDÜ Fen Bilimleri Enstitüsü Dergisi, 17.
  79. Özkan, G. & İnal, M. (2014). “Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems”. Applied Soft Computing, 24, 232-238. doi:10.1016/j.asoc.2014.06.032
  80. Oztekin, A., Kizilaslan, R., Freund S & Iseri, A. (2016). “A data analytic approach to forecasting daily stock returns in an emerging market”. European Journal of Operational Research, 253(3), 697-710. doi:10.1016/j.ejor.2016.02.056
  81. Pao, H. T. (2008). “A Comparison of Neural Network and Multiple Regression Analysis in Modeling Capital Structure”. Expert Systems with Applications, 720-727.
  82. Permai, S. D., Tanty, H., & Rahayu, A. (2016), “Geographically Weighted Regression Analysis for Human Development Index”. AIP Conference Proceedings, 1-7
  83. Rajpal, P., Shishodia, K. & Sekhon, G. (2006). “An Artificial Neural Network for Modeling Reliability, Availability and Maintainability of a Repairable System”. Reliability Engineering and System Safety, 809-819.
  84. Roham, M., Gabrielyan, A. R. & Archer, N. P. (2012). “Predicting the impact of hospital health information technology adoption on patient satisfaction”. Artif Intell Med, 56(2), 123-135. doi:10.1016/j.artmed.2012.08.001
  85. Schumacher, M., Robner, R. & Vach, W. (1996). “Neural Networks and Logistic Regression: Part I.” Computational Statistics and Data Analysis, 661-682.
  86. Şen, Z. (2004). “Yapay Sinir Ağları İlkeleri”, İstanbul: Su Vakfı Yayınları
  87. Şengöz, N. & Özdemir, G. (2016). “Sınıflandırma Problemlerinin Karşılaştırılmasında ANFIS ve Basamak Korelasyon Sinir Ağının Kullanımı” Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi.
  88. Shekarian, E. & Gholizadeh, A. A. (2013). “Application of adaptive network based fuzzy inference system method in economic welfare”. Knowledge-Based Systems, 39, 151-158. doi:10.1016/j.knosys.2012.10.013
  89. Toprak, E. (2017), “Yapay Sinir Ağı, Karar Ağaçları ve Ayırma Analizi Yöntemleri ile PISA 2012 Matematik Başarılarının Sınıflandırılma Performanslarının Karşılaştırılması”
  90. Tu, J. V. (1996). “Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes”. J. Clin Epidemiol, 1225-1231.
  91. Vasileva-Stojanovska, T., Vasileva, M., Malinovski, T. & Trajkovik, V. (2015). “An ANFIS model of quality of experience prediction in education”. Applied Soft Computing, 34, 129-138. doi:10.1016/j.asoc.2015.04.047
  92. Wang, Y. & Elhag, T. (2008). “An adaptive neuro-fuzzy inference system for bridge risk assessment”. Expert Systems with Applications, 34(4), 3099-3106. doi:10.1016/j.eswa.2007.06.026
  93. Wu, T.-H., Huang, Y.-I. & Chen, J.-M. (2015). “Development of an adaptive neural-based fuzzy inference system for feeding decision-making assessment in silver perch (Bidyanus bidyanus) culture”. Aquacultural Engineering, 66, 41-51. doi:10.1016/j.aquaeng.2015.02.001
  94. Yee, A. & Chong, L. (2013).” A Two-Staged SEM-Neural Network Approach for Understanding and Predicting the Determinants of M-Commerce Adoption”. Expert Systems with Applications, 1240-1247.
  95. Yılmazer, M. (2002). “İnsani kalkınma politikaları ve Türkiye üzerine bir deneme”. Aydın: Adnan Menderes Üniversitesi.
  96. Zadeh, L. A. (1965). “Fuzzy sets”. Information and control, 8(3), 338-353.
  97. Zhang, G., Hu, M., Patuwo, E. & Indro, D. (1999). “Artificial Neural Networks in Bankruptcy Prediction: General Framework And Cross-Validation Analysis.” Europian Journal of Operational Research, 16-32