F-WSS $$^{++}$$ + + : incremental wrapper subset selection algorithm for fuzzy extreme learning machine

Abstract

Fuzzy extreme learning machine (F-ELM) is a hybrid combination made to get the benefits of fuzzy system and extreme learning machine (ELM). F-ELM randomly initializes the weights between input layer to the hidden layer and analytically tunes the weights between hidden layer to the output layer. Due to this random initialization and the availability of redundant and irrelevant features, F-ELM may degrade the overall (generalization) performance. To solve the mentioned problem in this paper, an advanced classification algorithm $$\hbox {F-WSS}^{++}$$ (incremental wrapper subset selection algorithm for F-ELM) is designed for multiclass and binary class classification problems. The merits of the proposed algorithm are analyzed theoretically and experimentally. The test results are cross checked for $$\hbox {F-WSS}^{++}$$ and $$\hbox {E-WSS}^{++}$$ (incremental wrapper subset selection algorithm for ELM) for the clinical dataset. The effectiveness of the $$\hbox {F-WSS}^{++}$$ algorithm is verified by statistical methods. It is observed that $$\hbox {F-WSS}^{++}$$ has the capability to handle weighted classification problem, feature subset selection problem and optimization problem. It also improves 9–10% classification accuracy by using only 50% features.