Hybridization of Fuzzy Min-Max Neural Networks with kNN for Enhanced Pattern Classification.

EasyChair Preprint no. 5073

Abstract

Fuzzy Min-Max Neural Networks (FMNN) is a single epoch learning Pattern Classification algorithm with several advantages for online learning. The information loss due to Contraction step of FMNN leads to several improvements in literature such as MLF, FMCN etc. These approaches do not use Contraction step and provide additional structures in FMNN for decision making in overlapped regions overcoming the problem of Contraction with the cost of an increase in training complexity of FMNN.This work proposes a hybridization of FMNN with kNN algorithm for achieving the ability to handle decision making in overlapped regions without altering the structure of FMNN. Comparative studies with existing approaches over benchmark decision systems have proved the utility of the proposed kNN-FMNN approach.

Keyphrases: Classification, FMNN, fuzzy min-max neural network, fuzzy sets, hybrid system, KNN, MLF, neural networks

@Booklet{EasyChair:5073,
  author = {Anil Kumar and P.S.V.S Sai Prasad},
  title = {Hybridization of Fuzzy Min-Max Neural Networks with kNN for Enhanced Pattern Classification.},
  howpublished = {EasyChair Preprint no. 5073},

  year = {EasyChair, 2021}}