Weighted extreme learning machine for imbalance learning

Abstract

Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are “generalized” single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able to generalize to balanced data. The proposed method maintains the advantages from original ELM: (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users' needs, the weighted ELM can be generalized to cost sensitive learning.