Outlier Robust Extreme Learning Machine for Multi-Target Regression · arXivDesk
1905.09368May 22, 2019Submitted to Expert Systems With Applications in April 16, 2019. Currently under review. Highlights: Handles Multi-Target Regression Problems, Generalized Outlier Robust Extreme Learning Machine, Shows robustness when training data is contaminated with outliers, Training time is similar to Generalized and Outlier-Robust Extreme Learning Machines, An incremental version of the method is presented
Outlier Robust Extreme Learning Machine for Multi-Target Regression
Bruno Légora Souza da Silva, Fernando Kentaro Inaba, Evandro Ottoni Teatini Salles, Patrick Marques Ciarelli
The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based on minimizing the ℓ2 norm of the error, which is not suitable to deal with outliers, essentially in regression tasks. The use of ℓ1 norm was proposed in Outlier Robust ELM (OR-ELM), which is defined to one-dimensional outputs. In this paper, we generalize OR-ELM to deal with multi-target regression problems, using the error ℓ2,1
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norm and the Elastic Net theory, which can result in a more sparse network, resulting in our method, Generalized Outlier Robust ELM (GOR-ELM). We use Alternating Direction Method of Multipliers (ADMM) to solve the resulting optimization problem. An incremental version of GOR-ELM is also proposed. We chose 15 public real-world multi-target regression datasets to test our methods. Our conducted experiments show that they are statistically better than other ELM-based techniques, when considering data contaminated with outliers, and equivalent to them, otherwise.