Input significance analysis: feature selection through synaptic weights manipulation for EFuNNs classifier

  • R. Hassan
  • I.F.T. Al-Shaikhli
  • S. Ahmad
Keywords: feature selection, feature ranking, input significance analysis, evolving connectionist systems, evolving fuzzy neural network, connection weights, Garson’s algorithm.

Abstract

This work is interested in ISA methods that can manipulate synaptic weights namely
Connection Weights (CW) and Garson’s Algorithm (GA) and the classifier selected is
Evolving Fuzzy Neural Networks (EFuNNs). Firstly, it test FS method on a dataset selected
from the UCI Machine Learning Repository and executed in an online environment, record
the results and compared with the results that used original and ranked data from the previous
work. This is to identify whether FS can contribute to improved results and which of the ISA
methods mentioned above that work well with FS, i.e. give the best results. Secondly, to attest
the FS results by using a differently selected dataset taken from the same source and in the
same environment. The results are promising when FS is applied, some efficiency and
accuracy are noticeable compared to the original and ranked data.

Keywords: feature selection; feature ranking; input significance analysis; evolving
connectionist systems; evolving fuzzy neural network; connection weights; Garson’s
algorithm.

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eISSN: 1112-9867