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This article is cited in 1 scientific paper (total in 1 paper)
Analysis of textual and graphical information
On the complexity of characteristic function sets for correct diagnostic problem solving
M. I. Zabezhailo Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
Abstract:
There are discussed some abilities and tools to evaluate the quality of results of intelligent data analysis (IDA) for diagnostic type tasks. Reliability (non-falsifiability) of empirical dependencies formed in the process of machine learning (interpolation-extrapolation) by examples/precedents is explored by means of so-called Characteristic Functions (partially defined logical functions). The process of ChFs generation from training sample of examples is based on analysis of precedents description similarities formalized as binary algebraic operation. To provide a diagnostics correctness a method for evaluating a training sample representativeness is proposed. This method is based on diagnosis causality analysis provided by the used IDA tools. Some estimates of ChFs computational complexity are presented.
Keywords:
artificial intelligence, intelligent data analysis, diagnostics, empirical dependencies, causality analysis, computational complexity.
Citation:
M. I. Zabezhailo, “On the complexity of characteristic function sets for correct diagnostic problem solving”, Artificial Intelligence and Decision Making, 2021, no. 2, 44–54
Linking options:
https://www.mathnet.ru/eng/iipr100 https://www.mathnet.ru/eng/iipr/y2021/i2/p44
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