Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data

Journal Title: International Journal of Intelligent Engineering and Systems - Year 2017, Vol 10, Issue 2

Abstract

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

Authors and Affiliations

Harshita Patel

Keywords

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  • EP ID EP229419
  • DOI 10.22266/ijies2017.0430.17
  • Views 109
  • Downloads 0

How To Cite

Harshita Patel (2017). Improved Fuzzy-Optimally Weighted Nearest Neighbor Strategy to Classify Imbalanced Data. International Journal of Intelligent Engineering and Systems, 10(2), 156-162. https://europub.co.uk/articles/-A-229419