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.