LARSEN-ELM: Selective Ensemble of Extreme Learning Machines using LARS for Blended Data

TL;DR

LARSEN-ELM enhances the robustness of extreme learning machines on blended data by combining variable selection via LARS and a genetic algorithm-based ensemble, outperforming existing methods in accuracy and speed.

Contribution

It introduces a novel framework integrating LARS for feature selection and GA-based ensemble for ELM, improving robustness on blended datasets.

Findings

LARSEN-ELM outperforms original ELM and other methods in robustness.

The approach maintains high speed while improving accuracy.

Experimental results on UCI datasets validate effectiveness.

Abstract

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called LARSEN-ELM for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, preprocessing, we select the input variables highly related to the output using least angle regression (LARS). In the second step, training, we employ Genetic Algorithm (GA) based selective ensemble and original ELM. In the experiments, we apply a sum of two sines and four datasets from UCI repository to verify the robustness of our approach. The experimental results show that compared with original ELM and other methods such as OP-ELM, GASEN-ELM and LSBoost, LARSEN-ELM significantly improve robustness performance while keeping a relatively high speed.