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

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.