Knowledge Granulation Research Articles

Evolving Granules for Classification for Discovering Difference in the Usage of Words

This paper proposes an evolutionary approach for discovering difference in the usage of words to facilitate collaboration among people. In general, different people seem to have different ways of conception and thus can have different concepts even on the same thing. When people try to communicate their concepts with words, such difference in the meaning and usage can lead to misunderstanding in communication, which can hinder their collaboration. In our approach each granule of knowledge in classification from users is structured into a decision tree so that difference in the usage of words can be discovered as difference in the structure of decision trees. By treating each granule of classification knowledge (i.e., decision tree) as an individual in Genetic Algorithm (GA), evolution is carried out with respect to both classification efficiency of each individual and diversity as a population so that the granule for classification is gradually evolved with diverse structure. Experiments were carried out on motor diagnosis cases with artificially encoded difference in the usage of words and the result shows the effectiveness of the proposed evolutionary approach.

Rough Mereological Calculi of Granules: A Rough Set Approach To Computation

Rough Mereology is a paradigm allowing for a synthesis of main ideas of two potent paradigms for reasoning under uncertainty: Fuzzy Set Theory and Rough Set Theory. Approximate reasoning is based in this paradigm on the predicate of being a part to a degree. We present applications of Rough Mereology to the important theoretical idea put forth by Lotfi Zadeh (1996, 1997), i.e., Granularity of Knowledge: We define granules of knowledge by means of the operator of mereological class and we extend the idea of a granule over complex objects like decision rules as well as decision algorithms. We apply these notions and methods in the distributed environment discussing complex problems of knowledge and granule fusion. We express the mechanism of complex granule formation by means of a formal grammar called Synthesis Grammar defined over granules of knowledge, granules of classifying rules, or over granules of classifying algorithms. We finally propose hybrid rough‐neural schemes bridging rough and neural computations.