Combination of OSELM classifiers with fuzzy integral for large scale classification

Abstract

Abstract

This paper proposed a large scale classification approach, which combines OSELM (Online Sequential Extreme Learning Machine) classifiers with fuzzy integral. The proposed method consists of three steps, (1) Firstly the component OSELM classifiers are sequentially trained on subsets of a large data set, in the process of training component classifiers, the instances previously used will be excluded from training the following component classifiers. (2) The trained component classifiers are combined with fuzzy integral. (3) The aggregation learning system is used for classifying the unseen samples. We compared our method with two other state-of-the-art large data sets classification methods, which are DTSVM (Decision Tree Support Vector Machine) and CVM (Core Vector Machine). The experimental results show that the proposed method outperforms DTSVM and CVM. Moreover the proposed approach can overcome instability of OSELM in different trials of simulations.