Optimum Parameters Selection Using Bacterial Foraging Optimization for Weighted Extreme Learning Machine

Extreme Learning Machine (ELM) is a Single Layer Feed Forward Network (SLFN) model with extremely learning capacity and good generalization capabilities. Generally, the performance of ELM for classification task highly based on three factors such as the input weight matrix, the value of bias and the number of hidden neurons presented. ELM randomly chooses the input weights and biases and determines analytically the weights as output. The random selection of biases and the input weight produce an unforeseen result which causes training error and also produces lesser prediction accuracy. Bacterial Foraging Optimization algorithm (BFOA) was used to find the optimum input weight and hidden bias values for ELM. With the unequal distribution of classes in imbalanced data sets, ELM algorithms tussle to find good accuracy. So, ELM algorithm doesn’t get the necessary information about the minority class to make an accurate classification. To deal the issues associated with ELM, in this paper the hybrid algorithms Weighted ELM and Weighted ELM with BFO are proposed. Weighted ELM is proposed to handle the classification data that has imbalanced nature of class distribution. The main objective of weighted ELM is that the related weight value is computed and assigned for each training sample to increase the classification rate. Bacterial Foraging Optimization method is also integrated with the weighted ELM to find the optimum input weight and bias to maximize the classification accuracy. The comparative analysis has been performed over Hepatitis dataset. Further, the experimental results clearly revealed that one of the proposed methods Weighted ELM with BFO performs quite well when compared to others.