Condensed Nearest Neighbor Decision Rule Input Weight Sequential Feed-Forward Neural Networks

Abstract

This paper proposes a new approach is referred to as condensed nearest neighbor decision rule (CNN) input weight sequential feed-forward neural networks (CIW-SFFNS). In this paper, it is firstly shown that the difference of optimization constraints between the extreme learning machine (ELM) and constrained-optimization-based extreme learning machine. For the second time, this paper proposes a method that using CNN to select the hidden-layer weights from example. Moreover, we compare error minimized extreme learning machines (EM-ELM), support vector sequential feed-forward neural networks (SV-SFFNS) and CIW-SFFNS from two aspects:test accuracy and the number of hidden nodes. We present the result of an experimental study on 10 classification sets. The CIW-SFFNS algorithm has a statistically significant improvement in generalization performance than EM-ELM and SV-SFFNS.