KNOWLEDGE EXTRACTION BASED ON DECISION TREES AND STOCHASTIC SEARCH

Authors

  • A.A. Oliinyk Zaporizhzhya National Technical University, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2014-2-16

Keywords:

sample, decision tree, model of quality control, production rule, stochastic search.

Abstract

The problem of mathematical support development is solved to automate the extraction knowledge as production rules from the training data samples. The object of study is the process of constructing models of non-destructive quality control. The subject of study are methods of production rules extraction for synthesis of quality control models. The purpose of the work is to improve the efficiency of the process of production rules extraction for constructing models of quality control based on training samples. The stochastic method for the decision trees synthesis is proposed, which uses information about the informativeness of features, the complexity of the synthesized tree, as well as the accuracy of its recognition, which allows to form on the initial stage a set of tree structures, characterized by a simple hierarchy and low error recognition, in the process of search to create a new set of solutions with taking into account information about the significance of the features and interpretability of generated trees, which, in turn, provides the possibility of constructing a decision tree with a small number of elements (nodes and branches between them), and an acceptable recognition accuracy and retrieval based on it the most valuable instances. The software
implementing proposed method is developed. The experiments to study the properties of the proposed method are conducted. The experimental results allow to recommend the proposed method for use in practice.

References

Ding S. X. Model-based fault diagnosis techniques: design schemes, algorithms, and tools / S. X. Ding. – Berlin: Springer, 2008. – 473 p. 2. Rutkowski L. Flexible neuro-fuzzy systems : structures, learning and performance evaluation / L. Rutkowski. – Boston : Kluwer, 2004. – 276 p. 3. Нейро-фаззи сети Петри в задачах моделирования сложных систем / [Е. В. Бодянский, Е. И. Кучеренко, А. И. Михалев] – Днепропетровск : Системные технологии. – 2005. – 311 с. 4. Jang J. R. ANFIS: Adaptive-network-based fuzzy inference system / J. R. Jang // IEEE transactions on systems and cybernetics. – 1993. – Vol. 23. – P. 665–685. DOI: 10.1109/21.256541. 5. Mulaik S. A. Foundations of Factor Analysis / S. A. Mulaik. – Boca Raton, Florida: CRC Press. – 2009. – 548 p. 6. Jensen R. Computational intelligence and feature selection: rough and fuzzy approaches / R. Jensen, Q. Shen. – Hoboken: John Wiley & Sons, 2008. – 339 p. 7. Abonyi J. Cluster analysis for data mining and system identification / J. Abonyi, B. Feil. – Basel : Birkhäuser, 2007. – 303 p. 8. Rokach L. Data Mining with Decision Trees. Theory and Applications / L. Rokach, O. Maimon. – London : World Scientific Publishing Co, 2008. – 264 p. DOI: 10.1142/9097. 9. Quinlan J. R. Induction of decision trees / J. R. Quinlan // Machine Learning. – 1986. –No. 1. – P. 81–106. DOI: 10.1007/ BF00116251. 10. Classification and regression trees / L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone. – California : Wadsworth & Brooks, 1984. – 368 p. 11. Интеллектуальные информационные технологии проекти- рования автоматизированных систем диагностирования и распознавания образов : монография / [С. А. Субботин, Ан. А. Олейник, Е. А. Гофман, С. А. Зайцев, Ал. А. Олейник ; под ред. С. А. Субботина. – Харьков] : ООО «Компания Смит», 2012. – 317 с. 12. Yu X. Introduction to Evolutionary Algorithms (Decision Engineering) / X. Yu, M. Gen. – London: Springer, 2010. – 418 p. DOI: 10.1007/978-1-84996-129-5. 13. Gen M. Genetic algorithms and engineering design / M. Gen, R. Cheng. – New Jersey: John Wiley & Sons, 1997. – 352 p. DOI: 10.1002/9780470172254

Published

2014-09-26

How to Cite

Oliinyk, A. (2014). KNOWLEDGE EXTRACTION BASED ON DECISION TREES AND STOCHASTIC SEARCH. Radio Electronics, Computer Science, Control, (2). https://doi.org/10.15588/1607-3274-2014-2-16

Issue

Section

Neuroinformatics and intelligent systems