An Enhanced Extreme Learning Machine Based on Square-Root Lasso Method
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Extreme learning machine (ELM) is one of the most notable machine learning algorithms with many advantages, especially its training speed. However, ELM has some drawbacks such as instability, poor generalizability and overfitting in the case of multicollinearity in the linear model. This paper introduces square-root lasso ELM (SQRTL-ELM) as a novel regularized ELM algorithm to deal with these drawbacks of ELM. A modified version of the alternating minimization algorithm is used to obtain the estimates of the proposed method. Various techniques are presented to determine the tuning parameter of SQRTL-ELM. The method is compared with the basic ELM, RIDGE-ELM, LASSO-ELM and ENET-ELM on six benchmark data sets. Performance evaluation results show that the SQRTL-ELM exhibits satisfactory performance in terms of testing root mean squared error in benchmark data sets for the sake of slightly extra computation time. The superiority level of the method depends on the tuning parameter selection technique. As a result, the proposed method can be considered a powerful alternative to avoid performance loss in regression problems .
Açıklama
Anahtar Kelimeler
Extreme learning machine, Ridge, Lasso, Square-root lasso, Regression
Kaynak
Neural Processing Letters
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
56
Sayı
1
