Affiliations: [a] Institute of Aeronautical Engineering (AUTONOMOUS),, Dundigal 500043, India | [b] CBIT, Gandipet, Telangana | [c] JNTU, Kakinada, Andhra Pradesh
Correspondence:
[*]
Corresponding author: D. Kishore Babu, Institute of Aeronautical Engineering (AUTONOMOUS), Dundigal 500043, India.
E-mail: [email protected].
Abstract: In data stream classification, selecting the classifier for the dynamic feature space and considering the concept drift is a challenging task. This paper addresses the major challenges in the data stream classification with recurring concept drift. We developed a novel classification method known as Pearson Guassian Naïve Bayes classification (PGNBC). The proposed PGNBC method is the advancement over the existing Guassian Naïve Bayes classifier (GNBC) by additionally adding the correlation among the attributes. For the data stream classification, the proposed PGNBC is frequently updated based on the concept drift. This newly developed method is experimented by comparing the results with the existing methods such as RGNBC and MReC-DFS. The metrics such as sensitivity, specificity and accuracy are used for measuring the performance. It is found that the improvement in terms of sensitivity, specificity and accuracy values are better for the proposed method, with the values of 4%, 1% and 1% respectively, which is higher for the PGNBC method than the RGNBC method for the skin data. But with the localization data, the improvement in terms of specificity and accuracy values are 6% and 2% respectively which is higher than the RGNBC.
Keywords: Data stream, recurring concept drift, Naïve Bayes, rough set theory, classification
