Bayesci ağ yapısının öğrenilmesinde grafiksel bir yaklaşım

Yıl 2017, Cilt: 10 Sayı: 1, 1 - 10, 25.06.2017

Yasemin Kayhan Atılgan , Derya Ersel

Öz

Bu çalışmada, uzman bilgisi olmadığında Bayesci ağ yapısının öğrenilmesinde, çok boyutlu veri kümesindeki değişkenler arasındaki ilişki yapısını ortaya koyan Robust Coplot [1] grafiğinden yararlanılması önerilmiştir. Böylece, zaman alıcı öğrenme algoritmalarına gerek kalmadan Bayesci ağın oluşturulması sağlanmıştır. Önerilen yöntem bir veri kümesi üzerinde uygulanarak sonuçlar tartışılmış ve Robust Coplot grafiği ile veri kümesinin ön incelemesinin uzman bilgisi eksikliğini büyük ölçüde giderdiği gösterilmiştir.

Anahtar Kelimeler

Bayesci ağlar, Bayesci ağlarda öğrenme, Robust Coplot.

A Graphical approach to learning Bayesian networks

Öz

In this study, it is proposed to use Robust Coplot [1] that reveals the relationship structure between variables in multi-dimensional dataset to learn Bayesian network structure in the absence of expert knowledge. Hence, it is provided to create Bayesian networks without the need for time-consuming learning algorithms. The proposed method is applied to a data set and the results are discussed. Besides, it is shown that preliminary examination of data set with Robust Coplot provides to fulfill the deficiency of expert knowledge to a large extent

Anahtar Kelimeler

Bayesian networks, learning Bayesian Networks, Robust Coplot.

Kaynakça

• [1] Y. K. Atilgan, 2016, Robust Coplot Analysis, Journal Communications in Statistics - Simulation and Computation, 45 (5), 1763-1775.
• [2] Ben-Gal, 2007, Bayesian Networks, Encyclopedia of Statistics in Quality &Reliability, F. Ruggeri, F.Faltin, R. Kenett, R. (eds), Wiley & Sons.
• [3] S.G. Boettcher, C. Dethlefsen, C., 2003, deal: A package for learning Bayesian networks, Journal of Statistical Software, 8 (20), 1-40.
• [4] D. M. Bravata, K. G. Shojania, I. Oklin, A. Raveh, 2007, CoPlot: A tool for visualizing multivariate data in medicine, Statistics in Medicine, 27 (12), 2234-2247.
• [5] D. Chickering, D. Geiger, D. Heckerman, 1995, Learning Bayesian networks: Search methods and experimental results, Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, 112–128.
• [6] H. Demirhan, Y. K. Atilgan, 2015, New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique, Energy Conversion and Management, 106, 1013-1023.
• [7] Y. Dong-Peng, L. Jin-Lin, 2008, Research on personal credit evaluation model based on bayesian network and association rules, Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on, 457-460.
• [8] D. Ersel, 2012, An Original Combined Interestingness Measure in Association Analysis, Unpublished PhD Thesis, Hacettepe University Institute of Graduate Studies in Science, Ankara, Türkiye.
• [9] P. A. Ferero, G. B. Giannakis, 2011, Robust multi-dimensional scaling via outlier sparsity control, In: 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA. pp. 1183–1187.
• [10] Y. Goldreich, A. Raveh, 1993, COPLOT Display Technique as an Aid to Climatic Classification, Geographical Analysis, 25 (4), 337-353.
• [11] Y. Hadad, L. Friedman, M. Z. Hanani, 2007, Measuring Efficiency Of Restaurants Using the Data Envelopment Analysis Methodology, Applied Statistics Computer Modelling and New Technologies, 11 (4), 25-35.
• [12] D. Heckerman, 1995, Bayesian networks for data mining, Data Mining and Knowledge Discovery, 79-119.
• [13] F.V. Jensen, 2001, Bayesian Networks and Decision Graphs, Springer-Verlag, New York, 268p.
• [14] G. Lipshitz, A. Raveh, 1994, Application of the Co-plot method in the study of socioeconomic differences among cities: A basis for a differential development policy, Urban Studies, 31, 123-135.
• [15] B. Mahlberg, A. Raveh, 2012, Co-plot: A useful tool to detect outliers in DEA, Available at SSRN: http://ssrn.com/abstract=1999370 or http://dx.doi.org/10.2139/ssrn.1999370.
• [16] Netica, 2016, Testing Nets with Cases, Tutorial on Bayesian Networks with Netica, http://www.norsys.com/tutorials/netica/secD/tut_D2.htm.
• [17] A. Raveh, 2000, The Greek banking system: Reanalysis of performance, European Journal of Operational Research, 120, 525-534.
• [18] G. Shevlyakov, P. Smirnov, 2011, Robust estimation of the correlation coefficient: An attempt of survey, Austrian Journal of Statistics, 40, 147-156.
• [19] D. Talby, D. G. Feitelson, A. Raveh, 1999, Comparing logs and models of parallel workloads using the co-plot method, Lecture Notes in Computer Science, 1659, 43-66.
• [20] P. C. C. Wang, 1978, Graphical Representation of Multivariate Data, Akademic Press, New York