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/