HRFuzzy: Holoentropy-enabled rough fuzzy classifier for evolving data streams

Due to the 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 in data mining. Among the various tasks involved in data mining, the classification of data streams poses various challenging issues as compared to popular algorithms of data classification. 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 for 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. The performance is compared with the 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.