HRNeuro-fuzzy: Adapting neuro-fuzzy classifier for recurring concept drift of evolving data streams using rough set theory and holoentropy

Data stream; Neuro fuzzy; Change of detection; Rough set theory; Holoentropy function Abstract Data stream classification plays a vital role in data mining techniques which extracts the most important patterns from the real world database. Nowadays, many applications like sensor network, video surveillance and network traffic generate a huge amount of data streams. Due to the ambiguity in input data, imprecise input information and concept drift, some problems arise in classifying the data stream. To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function. At first, the input database is given to the PCA algorithm to reduce the dimension of the data. An adaptive neuro fuzzy classifier is utilized where the designing of membership function and rule base are the two important aspects. Then, neuro-fuzzy system undergoes updating when the change of detection occurs between the data streams. Here, the updating behaviour of membership function and rules are performed using rough set theory and holoentropy function. The experimental results are evaluated for the datasets and the performance is analysed by some metrics and compared with the existing systems such as JIT adaptive K-NN and HRFuzzy system. From the result, it is concluded that our proposed fuzzy classifier attains the higher accuracy of 96% which proves the efficient performance of data stream classification algorithm. 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CCBY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).