Assessment of data granulations in context of feature extraction problem

In this paper we investigate a method of measuring the quality of a data granulation in a decision system, defined by an indiscernibility relation in a specific type of approximation spaces. In the proposed algorithm, the concept of a random probe is used in order to estimate the probability that a given data granulation is relevant in a classification context. We explain an intuition behind our approach and show how it can be utilized for practical data analysis in tasks such as attribute selection or construction of new attributes. We also inspect relationships between the problem of finding a useful granulation of data and extracting informative features for supervised classification. To avoid low relevance of derived granules, we perform a random probe test to verify their validity. Using this technique we can more objectively assess the usefulness of a given data granulation for solving the classification problem at hand.