Details

Autor(en) / Beteiligte

• Mahmood, Saif F.
• Marhaban, Mohammad H.
• Rokhani, Fakhrul Z.
• Samsudin, Khairulmizam
• Arigbabu, Olasimbo Ayodeji

Titel

SVM–ELM: Pruning of Extreme Learning Machine with Support Vector Machines for Regression

Ist Teil von

• Journal of intelligent systems, 2016-10, Vol.25 (4), p.555-566

Ort / Verlag

Berlin: De Gruyter

Erscheinungsjahr

2016

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Extreme Learning Machine provides very competitive performance to other related classical predictive models for solving problems such as regression, clustering, and classification. An ELM possesses the advantage of faster computational time in both training and testing. However, one of the main challenges of an ELM is the selection of the optimal number of hidden nodes. This paper presents a new approach to node selection of an ELM based on a 1-norm support vector machine (SVM). In this method, the targets of SVM ∈{+1, –1} are derived using the mean or median of ELM training errors as a threshold for separating the training data, which are projected to SVM dimensions. We present an integrated architecture that exploits the sparseness in solution of SVM to prune out the inactive hidden nodes in ELM. Several experiments are conducted on real-world benchmark datasets, and the results attained attest to the efficiency of the proposed method.