A Robust Extreme Learning Machine for pattern classification with outliers

In this paper we introduce a simple and efficient extension of the Extreme Learning Machine (ELM) network (Huang et al., 2006 [19]), which is very robust to label noise, a type of outlier occurring in classification tasks. Such outliers usually result from mistakes during labeling of the data points (e.g. misjudgment of a specialist) or from typing errors during creation of data files (e.g. by striking an incorrect key on a keyboard). The proposed variant of the ELM, henceforth named Robust ELM (RELM), is designed using M-estimators to compute the output weights instead of the standard ordinary least squares (OLS) method. We evaluate the performance of the RELM using batch and recursive learning rules, and also introduce a model selection strategy based on Particle Swarm Optimization (PSO) to find an optimal architecture for datasets contaminated with non-Gaussian noise and outliers. By means of comprehensive computer simulations using synthetic and real-world datasets, we show that the proposed Robust ELM classifiers consistently outperforms the original version.