Larsen-Elm: Selective Ensemble of Extreme Learning Machines using Lars for Blended Data

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" to overcome 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 Improves Robustness Performance While Keeping a Relatively High Speed.

Department(s)

Engineering Management and Systems Engineering

Comments

Science and Technology Development Plan of Shandong Province, Grant 2008GG1055011

Keywords and Phrases

Extreme learning machine; LARS algorithm; LARSEN-ELM; Robustness; Selective ensemble

International Standard Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

03 Feb 2015

Link to Full Text

Share

 
COinS