Abstract
An actual task is the classification of knowledge of a specified subject area, where it’s represented not as information coded in a certain manner, but in a way close to the natural intelligence, which structures obtained knowledge according to a different principle. The well-known answers to the questions should be classified so that the current task could be solved. Thus a new method of decision tree formation, which is approximated to the natural intelligence, is suitable for knowledge understanding. The article describes how entropy is connected to knowledge appearance, classification of previous knowledge and with definitions used in decision trees. The latter is necessary for comparing the traditional methods with the algorithm of the decision tree obtaining approximated to the natural intelligence. The dependency of entropy on the properties of element subsets of a set has been obtained.
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References
Correa Bahnsen, A., Aouada, D., Ottersten, B.: Example-dependent cost-sensitive decision trees. Expert Syst. Appl. 42(19), 6609–6619 (2015)
Mantas Carlos, J., AbellĂ¡n, J.: Analysis and extension of decision trees based on imprecise probabilities: application on noisy data. Expert Syst. Appl. 41(5), 2514–2525 (2014)
Ferreira, D.R., Vasilyev, E.: Using logical decision trees to discover the cause of process delays from event logs. Comput. Ind. 70, 194–207 (2015)
Kamadi, V., Allam, A.R., Thummala, S.M., Nageswara Rao, V.P.: A computational intelligence technique for the effective diagnosis of diabetic patients using principal component analysis (PCA) and modified fuzzy SLIQ decision tree approach. Appl. Soft Comput. 49, 13–145 (2016)
Cui, Y.Q., Shi, J.Y., Wang, Z.L.: Analog circuit fault diagnosis based on quantum clustering based multi-valued quantum fuzzification decision tree (QC-MQFDT). Measurement 93, 421–434 (2016)
Popova, O., Popov, B., Romanov, D., Evseeva, M.: Optimel: software for selecting the optimal method. SoftwareX 6(C), 231–236 (2017). http://www.sciencedirect.com/science/article/pii/S2352711017300316?via%3Dihub
Popova, O., Popov, B., Karandey, V., Evseeva, M.: Intelligence amplification via language of choice description as a mathematical object (binary tree of question-answer system). Procedia – Soc. Behav. Sci. 214, 897–905 (2015). http://www.sciencedirect.com/science/article/pii/S1877042815061030
Popova, O., Romanov, D., Evseeva, M.: A new approximated to the natural intelligence decision tree. Int. J. Eng. Comput. Sci. (IJECS) 5(8), 17555–17561 (2016)
Popova, O., Shevtsov, Y., Popov, B., Karandey, V., Klyuchko, V.: Theoretical propositions and practical implementation of the formalization of structured knowledge of the subject area for exploratory research. In: Karwowski, W., Ahram, T. (eds.) Advances in Intelligent Systems and Computing 2018. IHSI2018, vol. 722, pp. 432–437. Springer, Dubai (2018)
Acknowledgments
The work was carried out with the financial support provided by the Russian Foundation for Humanities within the research projects No. 16-03-00382 within the theme “Monitoring the research activity of educational institutions in the conditions of information societyˮ of 18.02.2016.
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Popova, O., Shevtsov, Y., Popov, B., Karandey, V., Klyuchko, V., Gerashchenko, A. (2019). Entropy and Algorithm of the Decision Tree for Approximated Natural Intelligence. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2018. Advances in Intelligent Systems and Computing, vol 787. Springer, Cham. https://doi.org/10.1007/978-3-319-94229-2_30
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DOI: https://doi.org/10.1007/978-3-319-94229-2_30
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