@article{2947,
  author = {Jagannath Nalavade, Senthil Murugan},
  title = {HRFuzzy: Holoentropy-Enabled Rough Fuzzy Classifier for the Classification of Evolving Data Streams},
  journal = {Journal of Electronic Systems},
  year = {2020},
  volume = {10},
  number = {1},
  doi = {https://doi.org/10.6025/jes/2020/10/1/34-47},
  url = {http://www.dline.info/jes/fulltext/v10n1/jesv10n1_3.pdf},
  abstract = {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.},
}