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Fuzz-IEEE 2021

Narendra Babu Unnam pursuing Ph.D under the supervision of Prof. P Krishna Reddy presented a paper virtually on A Novel Parameter-Free Energy Efficient Fuzzy Nearest Neighbor Classifier for Time Series Data at  2021 IEEE International Conference on Fuzzy Systems (Fuzz-IEEE 2021: Handling Uncertainty in Interpretable Artificial Intelligence); Luxembourg from 11 – 14 July. The other authors of this paper are:  Penugonda Ravikumar (IIIT-RK, Valley), R Uday Kiran (University of AIZU), Yutaka Watanobe (University of AIZU), Kazuo Goda (University of Tokyo) and V Susheela Devi (Indian Institute of Science, Bangalore). Research work as explained by the authors:

Time  series  classification  is  an  important  model  in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature  of  events  (i.e.,  events  with  similar  values  may  belong  to different  classes)  within  the  data.  Consequently,  these  studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this   motivation,   this   paper   proposes   a   novel   fuzzy   nearest neighbor  classifier  for  time  series  data.  The  basic  idea  of  our classifier  is  to  transform  the  very  large  training  data  into  a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier outperforms the current parameter-free  time  series  classifiers  and  also  the  popular  deep learning  techniques.

More details on the conference at  https://attend.ieee.org/fuzzieee-2021/