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The Modified Method of Logical Analysis Used for Solving Classification Problems
Volume 29, Issue 3 (2018), pp. 467–486
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 October 2017
Accepted
1 June 2018
Published
1 January 2018

Abstract

The study is dictated by the need to interpret and justify the solutions of classification problems. In this context, a method of logical analysis of data is considered along with its modifications based on the specifically developed algorithmic procedures, the use of which can increase the interpretability and generalization capability of classifiers. The article confirms in an empirical way that the suggested optimization models are suitable for building informative patterns and that the designed algorithmic procedures are efficient when used for the method of logical analysis of data.

References

 
Alexe, G., Alexe, S., Axelrod, D., Boros, E., Hammer, P.L., Reiss, M. (2002). Combinatorial analysis of breast cancer data from image cytometry and gene expression microarrays. RUTCOR Technical Report, 3, 1–12.
 
Antamoshkin, A.N., Masich, I.S. (2006). Heuristic search algorithms for monotonic pseudo-Boolean function conditional optimization. Problems of Mechanical Engineering and Automation, 5(1), 55–61.
 
Antamoshkin, A.N., Masich, I.S. (2007a). Identification of pseudo-Boolean function properties. Problems of Mechanical Engineering and Automation, 2, 66–69.
 
Antamoshkin, A.N., Masich, I.S. (2007b). Pseudo-Boolean optimization in case of unconnected feasible sets. Models and Algorithms for Global Optimization, Series: Springer Optimization and Its Applications, 4(16), 111–122.
 
Antamoshkin, A., Semenkin, E. (1998). Local search efficiency when optimizing unimodal pseudoboolean functions. Informatica, 9(3), 279–296.
 
Bagirov, A.M. (2011). Fast modified global k-means algorithm for incremental cluster construction. Pattern Recognition, 44, 866–876.
 
Barsegyan, A.A., Kupriyanov, M.S., Stepanenko, V.V., Kholod, I.I. (2004). Method and Models of Data Analysis: OLAP and Data Mining. BHV-Peterburg, Saint Petersburg (in Russian).
 
Bonates, T., Hammer, P.L., Kogan, A. (2006). Maximum patterns in datasets. RUTCOR Research Report, 9, 1–18.
 
Boros, E., Hammer, P.L., Kogan, A., Crama, Y., Ibaraki, T., Makino, K. (2009). Logical analysis of data: classification with justification. RUTCOR Technical Report, 5, 1–34.
 
Brauner, M.W., Brauner, D., Hammer, P.L., Lozina, I., Valeyre, D. (2004). Logical analysis of computer tomography data to differentiate entities of idiopathic interstitial pneumonias. RUTCOR Research Report, 30, 1–17.
 
Golovenkin, S.E., Gorban, A.N., Schulman, B.A. et al. (1997). Complications of Myocardial Infarction: Database for Approbation of Recognition and Forecast Systems. Computing Center of Siberian Branch of Russian Academy of Sciences, Krasnoyarsk (in Russian).
 
Hammer, P.L., Bonates, T. (2005). Logical analysis of data: from combinatorial optimization to medical applications. RUTCOR Research Report, 10, 1–27.
 
Hammer, P.L., Kogan, A., Lejeune, M. (2004a). Modeling country risk ratings using partial orders. RUTCOR Research Report, 24, 1–30.
 
Hammer, P.L., Kogan, A., Simeone, B., Szedmak, S. (2004b). Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics, 144, 79–102.
 
Herrera, J.F.A., Subasi, M.M. (2013). Logical analysis of multi-class data. RUTCOR Technical Report, 5, 1–24.
 
Hwang, H.K., Choi, J.Y. (2015). Pattern generation for multi-class LAD using iterative genetic algorithm with flexible chromosomes and multiple populations. Expert Systems with Applications: An International Journal, 42(2), 833–843.
 
Kotsiantis, S.B. (2007). Supervised machine leaning: a review of classification techniques. Informatica, 31, 249–268.
 
Kuzmich, R., Masich, I. (2012). Building a classification model as a composition of informative patterns. Management Systems and Information Technologies, 2(48), 18–22. (in Russian).
 
Kuzmich, R., Masich, I. (2014). Modification to an objective function for building patterns aimed at increasing the distinction between the rules of the classification model. Management Systems and Information Technologies, 2(56), 14–18 (in Russian).
 
Provost, F., Hibert, C., Malet, J.-P. (2016). Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier. Geophysical Research Abstracts, 18, 23–35.
 
Rastrigin, L., Freymanis, E. (1988). Solving problems of multiple-scale optimization using random-search methods. Problems of Random Search, 11, 9–25 (in Russian).
 
Shi, K.-Q., Zhou, Y.-Y., Yan, H.-D., Li, H., Wu, F.-L., Xie, Y.-Y., Braddock, M., Lin, X.-Y., Zheng, M.-H. (2016). Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees. Journal of Viral Hepatitis, 24(2), 132–140.
 
Stupina, A., Ezhemanskaja, S., Kuzmich, R., Vaingauz, A., Korpacheva, L., Fyodorova, A. (2012). Multiple-attribute decision making method based on qualitative information. Modern Problems of Science and Education, 5, 1–8 (in Russian).
 
Sun, B., Chen, S., Wang, J., Chen, H. (2016). A robust multi-class AdaBoost algorithm for mislabeled noisy data. Knowledge-Based Systems, 102, 87–102.
 
Vijayarani, S., Divya, M. (2011). An efficient algorithm for generating classification rules. International Journal of Computer Science and Technology, 2(4), 512–515.
 
Vorontsov, K. (2010). Lectures on logical algorithms of classification. Access mode: http://www.machinelearning.ru/wiki/images/3/3e/Voron-ML-Logic.pdf (in Russian).
 
Weka 3 (2015). Data Mining with Open Source Machine Learning Software in Java. Access mode: http://www.cs.waikato.ac.nz/˜ml/weka/index.html.

Biographies

Kuzmich Roman
romazmich@gmail.com

R. Kuzmich is a candidate of technical sciences, an associate professor of Siberian Federal University (Krasnoyarsk, Russia). His research interests are optimization techniques, modelling, control systems.

Stupina Alena
h677hm@gmail.com

A. Stupina is a doctor of technical sciences, a professor of Siberian Federal University (Krasnoyarsk, Russia). Her research interests are n-version programming, modelling, control systems.

Korpacheva Larisa
korp_0777@mail.ru

L. Korpacheva is a candidate of technical sciences, an associate professor of Siberian Federal University (Krasnoyarsk, Russia). Her research interests are modelling, system analysis.

Ezhemanskaja Svetlana
sve-ta_ezh@inbox.ru

S. Ezhemanskaja is a candidate of technical sciences, an associate professor of Siberian Federal University (Krasnoyarsk, Russia). Her research interests are modelling, system analysis.

Rouiga Irina
irina_rouiga@bk.ru

I. Rouiga is a candidate of economical sciences, an associate professor of Siberian Federal University (Krasnoyarsk, Russia). Her research interests are economic-mathematical modelling, investment and innovation policy at the regional level.


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classification pattern degree coverage information content

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