Application of Random Sampling in the Concept-Dependent Granulation Method

Abstract

Professor Zadeh in his works proposed the idea of grouping similar objects on the basis of certain similarity measures, thus initiating the paradigm of granular computing. He made the assumption that similar objects may have similar decisions. This natural assumption, operates in other scientific methodologies, e.g. methods based on k nearest neighbours, in reasoning by analogy and in rough set theory. The above assumption implies the existence of grouped information nodes (granules) and has potential applications in reducing the size of decision systems. The hypothesis has guided" the creation of granulation techniques based on the use of rough inclusions (introduced by Polkowski and Skowron) -according to the scheme proposed by Polkowski. In their work, the possibility of a large reduction of the size of decision systems while maintaining the classification efficiency was verified in experimental works.In this paper, we investigate the possibility of using random sampling in the approximation of decision systems -as part of dealing with Big Data sets. We use concept-dependent granulation as a reference approximation method. Experiments on selected real-world data have shown a common regularity that gives a hint on how to apply random sampling for fast and effective size reduction of decision systems.

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“…In the referenced study [35], Cybulski and Artiemjew investigates the use of random sampling to approximate decision systems, emphasizing its application to Big Data challenges. The paper advocates for concept-dependent granulation as a reference method, with experiments on realworld data revealing consistent insights into effective random sampling for rapid decision system size reduction.…”