Abstract
References
- Entezari-Maleki R, Rezai A, Minaei-Bidgoli B. Comparison of classification methods based on the type of attributes and sample size. Journal of Convergence Information Technology 2009; 4.3: 94-102. [2] Han J, Kamber M. Data mining: Consepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, San Francisco, CA, USA: Elseiver, 2006.
- Kantardzic M. Data mining: concepts, models, methods, and algorithms University of California, California, CA, USA: Wiley-Interscience 2003.
- Yang Y, Liu X. A re-examination of text categorization methods. In: ACM conference on Research and Development in Information Retrieval; 1999; Berkeley, CA, USA, pp. 42-49.
- Komorowski J, Pawlak Z, Polkowski L, Skowron A. Rough fuzzy hybridization: a new trend in decision-making. Rough sets: a tutorial 1999; Singapore, Springer-Verlag.
- Yao Y. Probabilistic rough set approximations. International Journal of Approximate Reasoning 2008; 49:2, 255-271.
- Çekik R, Telçeken S, EKG Sinyallerinin Kaba Kümeler Teorisi Kullanılarak Sınıflandırılması. Anadolu University Journal of Science and Technology A-Applied Science and Engineering 2014;, 15.2: 125-135. [8] Ziarko W. Probabilistic approach to rough sets. International Journal of Approximate Reasoning 2008; 48:2, 272-284.
- Yao Y. Three-way decisions with probabilistic rough sets. Information Science 2010; 180:3, 341-353.
- Pawlak Z. Rough Sets. International Journal of Computer and Information Sciences 1982; 11: 341-356. [11] Pawlak Z. Rough sets: Theoretical aspects of reasoning about data. Vol. 9. Springer Science & Business Media, 2012.
- Walczak B. Massart DL. Rough Sets theory. Chemenometrics and Intelligent Laboratory Systems 1999; 47: 1-16. [13] Deng XF, Yao YY. A multifaceted analysis of probabilistic three-way decisions. Fundamenta Informaticae 2014; 132.3: 291-313.
- Hernandez-del-Olmo F, Gaudiso E. Evaluation of recommender system: a new approach. Expert Systems with Applications 2008; 35:3, 790-804.
- Herlocker JL, Konstan J.A, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 2004; 22:1, 5-53.
- Mark H, Eibe F, Geoffrey H, Bernhard P, Peter R, Ian HW (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
Abstract
Sınıflandırma, bilgisayar mühendisliğinde bir veri kümesinin, uzmanlar yerine bilgisayarlar tarafından, özellikleri aracılığı ile gruplanması işlemidir. Kaba kümeler teorisi son yıllarda sınıflandırma problemlerinde gerekli karar kurallarının belirlenmesinde etkili bir araç olarak kullanılmaktadır. Bu çalışmada Eskişehir Osmangazi Üniversitesi Tıp Fakültesi’ndeki kalp hastalarının EKG verileri Kaba Kümeler Teorisi (KKT) ve Olasılıksal Kaba Kümeler Teorisi (OKKT) yaklaşımı ile sınıflandırılmıştır. Sınıflandırmada elde edilen sonuçlar değerlendirilirken, doğruluk ve genellik kriterlerinden yararlanılmıştır. OKKT’de bu kriterler, bir nesnenin karar kümesi içerisinde olup olmadığının koşullu olasılığını temsil eden (α, β) eşik değerleriyle yakından ilişkili olduğu görülmüş ve ortalamaya bağlı OKKT’nin genellik değerinde %49 oranında iyileşme elde edilmiştir.
Keywords
Kaba kümeler teorisi Olasılıksal kaba kümeler teorisi EKG Sınıflandırma Uzman sistemler Veri madenciliği
References
- Entezari-Maleki R, Rezai A, Minaei-Bidgoli B. Comparison of classification methods based on the type of attributes and sample size. Journal of Convergence Information Technology 2009; 4.3: 94-102. [2] Han J, Kamber M. Data mining: Consepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, San Francisco, CA, USA: Elseiver, 2006.
- Kantardzic M. Data mining: concepts, models, methods, and algorithms University of California, California, CA, USA: Wiley-Interscience 2003.
- Yang Y, Liu X. A re-examination of text categorization methods. In: ACM conference on Research and Development in Information Retrieval; 1999; Berkeley, CA, USA, pp. 42-49.
- Komorowski J, Pawlak Z, Polkowski L, Skowron A. Rough fuzzy hybridization: a new trend in decision-making. Rough sets: a tutorial 1999; Singapore, Springer-Verlag.
- Yao Y. Probabilistic rough set approximations. International Journal of Approximate Reasoning 2008; 49:2, 255-271.
- Çekik R, Telçeken S, EKG Sinyallerinin Kaba Kümeler Teorisi Kullanılarak Sınıflandırılması. Anadolu University Journal of Science and Technology A-Applied Science and Engineering 2014;, 15.2: 125-135. [8] Ziarko W. Probabilistic approach to rough sets. International Journal of Approximate Reasoning 2008; 48:2, 272-284.
- Yao Y. Three-way decisions with probabilistic rough sets. Information Science 2010; 180:3, 341-353.
- Pawlak Z. Rough Sets. International Journal of Computer and Information Sciences 1982; 11: 341-356. [11] Pawlak Z. Rough sets: Theoretical aspects of reasoning about data. Vol. 9. Springer Science & Business Media, 2012.
- Walczak B. Massart DL. Rough Sets theory. Chemenometrics and Intelligent Laboratory Systems 1999; 47: 1-16. [13] Deng XF, Yao YY. A multifaceted analysis of probabilistic three-way decisions. Fundamenta Informaticae 2014; 132.3: 291-313.
- Hernandez-del-Olmo F, Gaudiso E. Evaluation of recommender system: a new approach. Expert Systems with Applications 2008; 35:3, 790-804.
- Herlocker JL, Konstan J.A, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 2004; 22:1, 5-53.
- Mark H, Eibe F, Geoffrey H, Bernhard P, Peter R, Ian HW (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | March 14, 2016 |
| Published in Issue | Year 2016 Volume: 17 Issue: 1 |
