A heuristic based dependency calculation technique for rough set theory

Abstract

Feature selection is the process of selecting subset of features that still provide maximum amount of the information that otherwise is provided by the entire set of conditional attributes. Many approaches have been proposed so far in literature for this purpose. Recently the rough set based approaches have become dominant. Majority of these approaches use attribute dependency to find significance of attributes. Problem with this measure is that it uses positive region to calculate dependency which is a computationally expensive job. As a consequence, it degrades the performance of the feature selection algorithms using this measure. In this paper, we have proposed a new heuristic based dependency calculation technique by avoiding the positive region. The proposed method uses a heuristics approach by finding the consistent records regarding each decision class in the dataset. Using this method, allows us to calculate dependency by avoiding the positive region, which ultimately enhances the computational efficiency of the underlying feature selection algorithm thus enabling it to be used for dataset beyond smaller size. In order to calculate dependency by using the proposed method, we have used a two-dimensional grid as intermediate data structure. Number of feature selection algorithms were used with proposed solution on various publically available datasets to justify it. A comparison framework was used to compare the proposed solution with conventional methods. Results have justified the proposed solution both in terms of its efficiency and effectiveness.