Analysis of ordering based ensemble pruning techniques for Voting based Extreme Learning Machine

Extreme Learning machine, ELM emerged as an efficient fast learning classifier for real valued classification problems. ELM suffers from the problem of instability due to random initialization of weights between the input and the hidden layer. Voting Based Extreme Learning Machine, VELM uses majority voting based ensembling technique to reduce this instability problem and improve the performance of ELM. VELM gives better performance at the cost of increased computational and memory requirement. This paper orders the component classifiers of the ensemble as per their importance using three different metrics: G-mean, Entropy and Margin based ordering. The pruned ensemble is constructed by selecting a threshold number of component classifiers as per their order of importance. The main aim of this work is to find which of these metric is more efficient for pruning VELM. This work presents an exhaustive analysis of these ordering metrics for varying sizes of pruned ensemble.