Generalization ability of Extreme Learning Machine using different Sample Selection Methods

Abstract

The recent explosion of data has triggered the need of data reduction for completing the effective data mining task in the process of knowledge discovery in databases (KDD). The process of instance selection (IS) plays a significant role for data reduction by eliminating the redundant, noisy, unreliable and irrelevant instances, which, in-turn reduces the computational resources, and helps to increase the capabilities and generalization abilities of the learning models. . This manuscript expounds the concept and functionalities of seven different instance selection techniques (i.e., ENN, AllKNN, MENN, ENNTh, Mul- tiEdit, NCNEdit, and RNG), and also evaluates their effectiveness by using single layer feed-forward neural network (SLFN), which is trained with extreme learning machine (ELM). Unlike traditional neural network, ELM randomly chooses the weights and biases of hidden layer nodes and analytically determines the weights of output layer node. The generalization ability of ELM is analyzed by using both original and reduced datasets. Experiment results depict that ELM provides better generalization with these IS methods.