Journal of Electronic Systems Jagannath Nalavade, Senthil Murugan 10 1 2020 https://doi.org/10.6025/jes/2020/10/1/34-47 http://www.dline.info/jes/fulltext/v10n1/jesv10n1_3.pdf Due to continuous growth of recent applications such as, telecommunication, sensor data, financial applications, analyzing of data streams, conceptually endless sequences of data records, frequently arriving at high rates is important task among the data mining community. Among the various task involved in data stream analysis, the classification of data streams pose various challenging issues compared to popular data classification algorithms. Since the classification algorithm performs endlessly, it must be able to adapt the classification model to handle the change of concept or boundaries between classes. In order to handle these issues, we have developed a new fuzzy system called, HRFuzzy to classification of evolving data streams. Here, rough set theory and holoentropy function are utilized to construct the dynamic classification model. In the fuzzy system, the rules are generated using k-means clustering and membership functions are dynamically updated using holoentropy function. The experimentation of the proposed HRFuzzy is performed using two different databases such as, skin segmentation dataset and localization data and the performance is compared with adaptive k-NN classifier in terms of accuracy and time. From the outcome, we proved that the proposed HRFuzzy outperformed in both the metrics by giving the maximum performance.