Yıl 2015,
, 0 - , 01.01.2015
Öz
— In this study, corporate bankruptcy prediction, a crucial economic problem is tackled. To do this, a data set of 240 distinct companies with financial features is used. This data set is applied to one of the most important classification and forecasting methods, i.e. decision tree method. Seven different decision tree algorithms are evaluated in terms of accuracy percentage, mean absolute error, root mean squared error, precision, recall, F-measure. According to experimental results, decision tree algorithms are appropriate methods for corporate bankruptcy prediction with relatively successful accuracy rates
Anahtar Kelimeler
— forecasting decision tree algorithms classification corporate bankruptcy
Kaynakça
- S. Sumathi, S.N. Sivanandam, Introduction to Data Mining and Its Applications, Springer-Verlag, Berlin, 200 F. Gorunescu, Data Mining: Concepts, Models and Techniques, Springer-Verlag, Berlin, 2011.
- J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kauffman, San Francisco, CA, USA, 19 I. Wayne, P. Langley, “Induction of One-Level Decision Trees”, Proceedings of the Ningth International Conference on Machine Learning, 233- 240, 1992.
- P. Domingos, P., G. Hulten, “Mining High-speed Data Streams”, Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71-80, 2000.
- N. Landwehr, M. Hall, E. Frank, “Logistic Model Trees”, Machine Learning, 59(1-2), 161-205, 2005.
- L. Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
- W. Fan, H. Wang, P. S. Yu, S. Ma, “Is Random Model Better? On Its Accuracy and Efficiency”, The Third IEEE International Conference on Data Mining, 51-58, 2003.
- Y. Zhao, Y. Zhang, “Comparison of Decision Tree Methods for Finding Active Objects”, Advances in Space Research, 41(12), 1955-1959, 2008.
- D. L. Olson, D. Delen, Y. Meng, “Comparative Analysis of Data Mining Methods for Bankruptcy Prediction”, Decision Support Systems, 52(2), 464-473, 20 M. Lam, “Neural Network Techniques For Financial Performance Prediction: Integrating Fundamental And Technical Analysis”, Decision Support Systems, 37(4), 567-581, 2004.
- S. Aoki, Y. Hosonuma, “Bankruptcy Prediction Using Decision Tree”, Proceedings of the Second Nikkei Econophysics Symposium, 299-302, 2004.
- M. F. Santos, P. Cortez, J. Pereira, H. Quintela, “Corporate bankruptcy prediction using data mining techniques”, WIT Transactions on Information and Communication Technologies, 37, 349-357, 2006.
- J. Neves, A. Vieria, “Improving Bankruptcy Prediction With Hidden Layer Learning: Vector Quantization”, The European Accounting Review, 15(2), 253-271, 2006.
- E. Alfaro, N. Garcia, M. Gamez, D. Elizondo, “Bankruptcy Forecasting: An Empirical Comparision of AdaBoost and Neural Networks”, Decision Support Systems, 45(1), 110-122, 2008.
- A. Nachev, “Fuzzy ARTMAP Neural Network for Classifying the Financial Health of a Firm”, Lecture Notes in Computer Science, 5027(2008), 82-91, 2008.
- S. Cho, H. Hong, B-C. Ha, “A Hybrid Approach Based On The Combination of Variable Selection Using Decision Trees And Case-Based Reasoning Using the Mahalanobis Distance: For Bankruptcy Prediction”, Expert Systems with Applications, 37(4), 3482-3488, 20
- H. R. Doolatabadi, S. M. Hoseini, R. Tahmasebi, “Using Decision Tree Model and Logistic Regression to Predict Companies Financial Bankruptcy in Tehran Stock Exchanges”, International Journal of Emerging Research in Management &Technology, 2(9), 7-16, 2013.
- E. Zibanezhad, B. Mobarake, D. Foroghi, “Applying decision tree to predict bankruptcy”, IEEE International Conference on Computer Science and Automation Engineering (CSAE), 165-169, 2011.
- Internet: V. Mohan, Decision Trees: A comparison of various algorithms http://cs.jhu.edu/~vmohan3/document/ai_dt.pdf, 02014. building decision trees,
- O., Maimon, L. Rokach, “Classification Trees”. Data Mining and Knowledge Discovery Handbook, Editör: O., Maimon, L. Rokach, Springer, New York, A.B.D., 149-175, 2010.
- J. Gehrke, “Decision Trees”, The Handbook of Data Mining, Editör: Nong Ye, Lawrence Erlbaum Associates Publishers, London, 149-175, 2003.
- P.J. García-Laencina, J.L. Sancho-Gómez, A.R. Figueiras-Vidal, “Pattern Classification with Missing Data: A Review”, Neural Comput. Appl., 19(2), 263-282, 20 D. Birant, “Comparison of Decision Tree Algorithms for Predicting Potential Air Pollutant Emissions with Data Mining Models”, Journal of Environmental Informatics, 17(1), 46-53, 2011.
- R. Kothari, M. Dong, “Decision Trees for Classification: A Review and Some New Results”, Pattern Recognition: From Classical to Modern Approaches, Editör: S. K. Pal, A. Pal, World Scientific, New Jersey, 2001.
- S. B. Kotsiantis, “Decision Trees: A Recent Overview”, Artificial Intelligence Review, 39(4), 261-283, 20 J. Quinlan, “Induction of Decision Trees”, Machine Learning, 1(1), 81-106, 1986.
- X. Niuniu, L. Yuxun, “Review of Decision Trees”, The Third IEEE International Conference on Computer Science and Information Technology, 105- 109, 2010.
- I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2. Baskı, San Elsevier, Francisco, 2005.
- P. Doetsch, C. Buck, P. Golik, N. Hoppe, M. Kramp, J. Laudenberg, C. Oberdörfer, P. Steingrube, J. Forster, A. Mauser, “Logistic Model Trees with AUCsplit Criterion for the KDD Cup 2009 Small Challenge”, JMLR:
- Workshop and Conference Proceedings, 77-88, 2009.
- W. Pietruszkiewicz, “Dynamical Systems and Nonlinear Kalman Filtering Applied in Classification”, Proceedings Conference on Cybernetic Intelligent Systems, 263- 268, 2008. 7th IEEE International http://en.wikipedia.org/wiki/Mean_absolute_error, 02014. Mean Absolute Error,
- K. Essig, H. Ritter, O. Strogan, T. Schack, “Influence of Movement Expertise on Visual Perception of Objects, Events and Motor Action: A Modeling Approach”, Developing and Applying Biologically-Inspired Vision Systems, Editor: Pomplun, M., Suzuki, J., IGI Global, Hershey, A.B.D., 1-30, 2013. http://en.wikipedia.org/wiki/Precision_and_recal, 02014. Precision and Recall,
Yıl 2015,
, 0 - , 01.01.2015
Öz
Bu çalışmada, önemli bir ekonomik problem olan, şirket iflaslarının tahminlenmesi ele alınmıştır. Bunun için iki yüz kırk farklı şirkete ilişkin finansal özellikleri içeren bir veri seti kullanılmıştır. Değinilen veri seti, sınıflandırma ve tahminlemede kullanılan önemli yöntemlerden biri olan karar ağacı yöntemine ilişkin yedi farklı algoritma ile uygulanarak, doğru sınıflandırma yüzdesi, ortalama mutlak hata, ortalama karesel hatanın karekökü, kesinlik, geri çağırma, F-ölçütü gibi ölçütler bakımından değerlendirilmiştir. Deneysel sonuçlar incelendiğinde, karar ağacı algoritmalarının şirket iflaslarının tahminlenmesi için uygun bir yöntem olduğu ve kısmen başarılı doğru sınıflandırma yüzdesi elde ettiği gözlemlenmiştir.
Anahtar Kelimeler
tahminleme karar ağacı algoritmaları sınıflandırma şirket iflasları
Kaynakça
- S. Sumathi, S.N. Sivanandam, Introduction to Data Mining and Its Applications, Springer-Verlag, Berlin, 200 F. Gorunescu, Data Mining: Concepts, Models and Techniques, Springer-Verlag, Berlin, 2011.
- J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kauffman, San Francisco, CA, USA, 19 I. Wayne, P. Langley, “Induction of One-Level Decision Trees”, Proceedings of the Ningth International Conference on Machine Learning, 233- 240, 1992.
- P. Domingos, P., G. Hulten, “Mining High-speed Data Streams”, Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 71-80, 2000.
- N. Landwehr, M. Hall, E. Frank, “Logistic Model Trees”, Machine Learning, 59(1-2), 161-205, 2005.
- L. Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
- W. Fan, H. Wang, P. S. Yu, S. Ma, “Is Random Model Better? On Its Accuracy and Efficiency”, The Third IEEE International Conference on Data Mining, 51-58, 2003.
- Y. Zhao, Y. Zhang, “Comparison of Decision Tree Methods for Finding Active Objects”, Advances in Space Research, 41(12), 1955-1959, 2008.
- D. L. Olson, D. Delen, Y. Meng, “Comparative Analysis of Data Mining Methods for Bankruptcy Prediction”, Decision Support Systems, 52(2), 464-473, 20 M. Lam, “Neural Network Techniques For Financial Performance Prediction: Integrating Fundamental And Technical Analysis”, Decision Support Systems, 37(4), 567-581, 2004.
- S. Aoki, Y. Hosonuma, “Bankruptcy Prediction Using Decision Tree”, Proceedings of the Second Nikkei Econophysics Symposium, 299-302, 2004.
- M. F. Santos, P. Cortez, J. Pereira, H. Quintela, “Corporate bankruptcy prediction using data mining techniques”, WIT Transactions on Information and Communication Technologies, 37, 349-357, 2006.
- J. Neves, A. Vieria, “Improving Bankruptcy Prediction With Hidden Layer Learning: Vector Quantization”, The European Accounting Review, 15(2), 253-271, 2006.
- E. Alfaro, N. Garcia, M. Gamez, D. Elizondo, “Bankruptcy Forecasting: An Empirical Comparision of AdaBoost and Neural Networks”, Decision Support Systems, 45(1), 110-122, 2008.
- A. Nachev, “Fuzzy ARTMAP Neural Network for Classifying the Financial Health of a Firm”, Lecture Notes in Computer Science, 5027(2008), 82-91, 2008.
- S. Cho, H. Hong, B-C. Ha, “A Hybrid Approach Based On The Combination of Variable Selection Using Decision Trees And Case-Based Reasoning Using the Mahalanobis Distance: For Bankruptcy Prediction”, Expert Systems with Applications, 37(4), 3482-3488, 20
- H. R. Doolatabadi, S. M. Hoseini, R. Tahmasebi, “Using Decision Tree Model and Logistic Regression to Predict Companies Financial Bankruptcy in Tehran Stock Exchanges”, International Journal of Emerging Research in Management &Technology, 2(9), 7-16, 2013.
- E. Zibanezhad, B. Mobarake, D. Foroghi, “Applying decision tree to predict bankruptcy”, IEEE International Conference on Computer Science and Automation Engineering (CSAE), 165-169, 2011.
- Internet: V. Mohan, Decision Trees: A comparison of various algorithms http://cs.jhu.edu/~vmohan3/document/ai_dt.pdf, 02014. building decision trees,
- O., Maimon, L. Rokach, “Classification Trees”. Data Mining and Knowledge Discovery Handbook, Editör: O., Maimon, L. Rokach, Springer, New York, A.B.D., 149-175, 2010.
- J. Gehrke, “Decision Trees”, The Handbook of Data Mining, Editör: Nong Ye, Lawrence Erlbaum Associates Publishers, London, 149-175, 2003.
- P.J. García-Laencina, J.L. Sancho-Gómez, A.R. Figueiras-Vidal, “Pattern Classification with Missing Data: A Review”, Neural Comput. Appl., 19(2), 263-282, 20 D. Birant, “Comparison of Decision Tree Algorithms for Predicting Potential Air Pollutant Emissions with Data Mining Models”, Journal of Environmental Informatics, 17(1), 46-53, 2011.
- R. Kothari, M. Dong, “Decision Trees for Classification: A Review and Some New Results”, Pattern Recognition: From Classical to Modern Approaches, Editör: S. K. Pal, A. Pal, World Scientific, New Jersey, 2001.
- S. B. Kotsiantis, “Decision Trees: A Recent Overview”, Artificial Intelligence Review, 39(4), 261-283, 20 J. Quinlan, “Induction of Decision Trees”, Machine Learning, 1(1), 81-106, 1986.
- X. Niuniu, L. Yuxun, “Review of Decision Trees”, The Third IEEE International Conference on Computer Science and Information Technology, 105- 109, 2010.
- I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2. Baskı, San Elsevier, Francisco, 2005.
- P. Doetsch, C. Buck, P. Golik, N. Hoppe, M. Kramp, J. Laudenberg, C. Oberdörfer, P. Steingrube, J. Forster, A. Mauser, “Logistic Model Trees with AUCsplit Criterion for the KDD Cup 2009 Small Challenge”, JMLR:
- Workshop and Conference Proceedings, 77-88, 2009.
- W. Pietruszkiewicz, “Dynamical Systems and Nonlinear Kalman Filtering Applied in Classification”, Proceedings Conference on Cybernetic Intelligent Systems, 263- 268, 2008. 7th IEEE International http://en.wikipedia.org/wiki/Mean_absolute_error, 02014. Mean Absolute Error,
- K. Essig, H. Ritter, O. Strogan, T. Schack, “Influence of Movement Expertise on Visual Perception of Objects, Events and Motor Action: A Modeling Approach”, Developing and Applying Biologically-Inspired Vision Systems, Editor: Pomplun, M., Suzuki, J., IGI Global, Hershey, A.B.D., 1-30, 2013. http://en.wikipedia.org/wiki/Precision_and_recal, 02014. Precision and Recall,
Toplam 28 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Ocak 2015 |
| Gönderilme Tarihi | 24 Ağustos 2014 |
| Yayımlandığı Sayı | Yıl 2015 |