Research Article
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COMPARISON STUDY OF DISTANCE MEASURES USING K- NEAREST NEIGHBOR ALGORITHM ON BANKRUPTCY PREDICTION

Year 2020, Volume: 19 Issue: 38, 224 - 233, 31.12.2020

Abstract

Machine learning is a discipline that is actively used in many areas such as medical, education and business management, from biotechnology to educational science, natural language processing to emotion analysis. As the area of use expanded, machine learning, which was looking for solutions to different problems such as regression, classing and clustering, also started to be used in the problem of bankruptcy prediction. As the number of algorithms increases in machine learning discipline, it is possible to achieve different accuracy rates as parameters change. For this purpose, the k Nearest Neighbor algorithm was involved in our study and the distance measure with the best accuracy were determined as a result of the classification process using different distance measures (Euclidean, Manhattan, Chebysev, Minkowski). The data set is divided into 70% training - 30% test; algorithms are compared using various performance criteria.

References

  • Alpaydin, E. (2010). Introduction to machine learning (2nd ed). MIT Press.
  • Altman, E. I., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy (Third Edition).
  • Back, B. (1996). Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms. 20.
  • Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001
  • Bulut, F., & Osmani, S. (2017). Scene Change Detection using Different Color Pallets and Performance Comparison. Balkan Journal of Electrical and Computer Engineering, 66-72. https://doi.org/10.17694/bajece.336217
  • Han, J., Kamber, M., & Pei, J. (2011). Data Mining Concepts and Techniques Third Edition (Third Edition). Morgan Kaufmann.
  • Hastie, T., friedman, J., & Tibshirani, R. (2008). Unsupervised Learning. In: The Elements of Statistical Learning. https://doi.org/10.1007/978-0-387-84858-7_14
  • Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 5(1), 1304. https://doi.org/10.1186/s40064-016-2941-7
  • Khan, M., Ding, Q., & Perrizo, W. (2002). K-nearest Neighbor Classification on Spatial Data Streams Using P-trees. Advances in Knowledge Discovery and Data Mining, 2336, 517-528. https://doi.org/10.1007/3-540-47887-6_51
  • Korol, T. (2019). Dynamic Bankruptcy Prediction Models for European Enterprises. Journal of Risk and Financial Management, 12(4), 185. https://doi.org/10.3390/jrfm12040185
  • Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. 20.
  • Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. https://doi.org/10.1016/j.ejor.2016.01.012
  • Mitchell, T. M. (2006). The Discipline of Machine Learning.
  • Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2019). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier—A Review. Big Data, 7(4), 221-248. https://doi.org/10.1089/big.2018.0175
  • Thian Cheng Lim, Lim Xiu Yun, J., Siwei, G., & Jiang, H. (2012). Bankruptcy Prediction: Theoretical Framework Proposal. International Journal of Management Sciences and Business Research, 1(9), 69-74.
  • UCI, https://archive.ics.uci.edu/ml/datasets/Taiwanese+Bankruptcy+Prediction, Erişim Tarihi: 25.08.2020
  • Weber, M. (2000). Unsupervised Learning of Models for Object Recognition. 127.
  • Weinberger, K. Q., Blitzer, J., & Saul, L. K. (t.y.). Distance Metric Learning for Large Margin Nearest Neighbor Classification. 8.

İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI

Year 2020, Volume: 19 Issue: 38, 224 - 233, 31.12.2020

Abstract

Makine öğrenmesi biyoteknoloji alanından eğitim bilimlerine, doğal dil işlemeden duygu analizine kadar medikal, eğitim, işletme gibi birçok alanda aktif olarak kullanılan bir disiplindir. Kullanım alanı genişledikçe regresyon, sınıflama, kümeleme gibi farklı problemlere çözüm arayan makine öğrenmesi, iflas tahmini probleminde de kullanılmaya başlamıştır. Makine öğrenmesi disiplininde algoritma sayısı arttıkça, parametreler değiştikçe farklı doğruluk oranlarına ulaşmak mümkündür. Bu amaçla, çalışmada k En Yakın Komşu algoritmasına yer verilmiş; farklı uzaklık ölçütleri (Euclidean, Manhattan, Chebysev, Minkowski) kullanılarak yapılan sınıflandırma işlemi sonucunda en yüksek doğruluk oranına sahip uzaklık ölçütü belirlenmiştir. Veri seti %70 eğitim- %30 test seti olarak bölünmüş; çeşitli performans ölçütleri kullanılarak algoritmalar birbiriyle karşılaştırılmıştır.

References

  • Alpaydin, E. (2010). Introduction to machine learning (2nd ed). MIT Press.
  • Altman, E. I., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy (Third Edition).
  • Back, B. (1996). Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms. 20.
  • Balcaen, S., & Ooghe, H. (2006). 35 years of studies on business failure: An overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38(1), 63-93. https://doi.org/10.1016/j.bar.2005.09.001
  • Bulut, F., & Osmani, S. (2017). Scene Change Detection using Different Color Pallets and Performance Comparison. Balkan Journal of Electrical and Computer Engineering, 66-72. https://doi.org/10.17694/bajece.336217
  • Han, J., Kamber, M., & Pei, J. (2011). Data Mining Concepts and Techniques Third Edition (Third Edition). Morgan Kaufmann.
  • Hastie, T., friedman, J., & Tibshirani, R. (2008). Unsupervised Learning. In: The Elements of Statistical Learning. https://doi.org/10.1007/978-0-387-84858-7_14
  • Hu, L.-Y., Huang, M.-W., Ke, S.-W., & Tsai, C.-F. (2016). The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 5(1), 1304. https://doi.org/10.1186/s40064-016-2941-7
  • Khan, M., Ding, Q., & Perrizo, W. (2002). K-nearest Neighbor Classification on Spatial Data Streams Using P-trees. Advances in Knowledge Discovery and Data Mining, 2336, 517-528. https://doi.org/10.1007/3-540-47887-6_51
  • Korol, T. (2019). Dynamic Bankruptcy Prediction Models for European Enterprises. Journal of Risk and Financial Management, 12(4), 185. https://doi.org/10.3390/jrfm12040185
  • Kotsiantis, S. B. (2007). Supervised Machine Learning: A Review of Classification Techniques. 20.
  • Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. https://doi.org/10.1016/j.ejor.2016.01.012
  • Mitchell, T. M. (2006). The Discipline of Machine Learning.
  • Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O., Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2019). Distance and Similarity Measures Effect on the Performance of K-Nearest Neighbor Classifier—A Review. Big Data, 7(4), 221-248. https://doi.org/10.1089/big.2018.0175
  • Thian Cheng Lim, Lim Xiu Yun, J., Siwei, G., & Jiang, H. (2012). Bankruptcy Prediction: Theoretical Framework Proposal. International Journal of Management Sciences and Business Research, 1(9), 69-74.
  • UCI, https://archive.ics.uci.edu/ml/datasets/Taiwanese+Bankruptcy+Prediction, Erişim Tarihi: 25.08.2020
  • Weber, M. (2000). Unsupervised Learning of Models for Object Recognition. 127.
  • Weinberger, K. Q., Blitzer, J., & Saul, L. K. (t.y.). Distance Metric Learning for Large Margin Nearest Neighbor Classification. 8.
There are 18 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

Gizem Dilki 0000-0002-2316-8928

Özlem Deniz Başar 0000-0002-9430-8975

Publication Date December 31, 2020
Submission Date November 20, 2020
Published in Issue Year 2020 Volume: 19 Issue: 38

Cite

APA Dilki, G., & Deniz Başar, Ö. (2020). İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 19(38), 224-233.
AMA Dilki G, Deniz Başar Ö. İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. December 2020;19(38):224-233.
Chicago Dilki, Gizem, and Özlem Deniz Başar. “İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 19, no. 38 (December 2020): 224-33.
EndNote Dilki G, Deniz Başar Ö (December 1, 2020) İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 19 38 224–233.
IEEE G. Dilki and Ö. Deniz Başar, “İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI”, İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 19, no. 38, pp. 224–233, 2020.
ISNAD Dilki, Gizem - Deniz Başar, Özlem. “İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 19/38 (December 2020), 224-233.
JAMA Dilki G, Deniz Başar Ö. İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2020;19:224–233.
MLA Dilki, Gizem and Özlem Deniz Başar. “İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI”. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 19, no. 38, 2020, pp. 224-33.
Vancouver Dilki G, Deniz Başar Ö. İŞLETMELERİN İFLAS TAHMİNİNDE K- EN YAKIN KOMŞU ALGORİTMASI ÜZERİNDEN UZAKLIK ÖLÇÜTLERİNİN KARŞILAŞTIRILMASI. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi. 2020;19(38):224-33.