A fast pruned-extreme learning machine for classification problem

By Hai-Jun Rong, Yew-Soon Ong, Ah-Hwee Tan, Zexuan Zhu. Neurocomputing, 2008

367 citations recorded by the source corpus.

Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification. In particular, we describe the proposed pruned-ELM (P-ELM) algorithm as a systematic and automated approach for designing ELM classifier network. P-ELM uses statistical methods to measure the relevance of hidden nodes. Beginning from an initial large number of hidden nodes, irrelevant nodes are then pruned by considering their relevance to the class labels. As a result, the architectural design of ELM network classifier can be automated. Empirical study of P-ELM on several commonly used classification benchmark problems and with diverse forms of hidden node functions show that the proposed approach leads to compact network classifiers that generate fast response and robust prediction accuracy on unseen data, comparing with traditional ELM and other popular machine learning approaches.

Subfields: Artificial Intelligence, Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1a7423f3c3386ec9e73a2e51a11f2afc61753cb7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. G. Huang
2. Shiji Song
3. Gao Huang
4. Yuan Lan
5. Keyou You
6. Dianhui Wang
7. Yanan Zhang
8. Shifei Ding

In Cancer Research

1. G. Huang

In Molecular Biology

1. Shifei Ding
2. G. Huang
3. Christian Jutten
4. Keyou You

Citations in the corpus

• Determination of Amino Acid Nitrogen in Soy Sauce Using Near Infrared Spectroscopy Combined with Characteristic Variables Selection and Extreme Learning Machine (2013, background): “Extreme learning machine (ELM), as an emergent nonlinear technique, has shown its excellent performance in classification (Rong et al. 2008) and regression problems (Yuan et al. 2011).”
• Trends in extreme learning machines: A review (2015, methodology): “In pruning ELM (P-ELM) (Miche et al., 2010; Rong, Ong, Tan, & Zhu, 2008), an initial network is built using traditional ELM, then those hidden neurons with less contribution to the training performance will be removed.”
• OP-ELM: Optimally Pruned Extreme Learning Machine (2010, methodology): “in [7], for classification purposes, and using statistical tests as a measure of relevance of the neurons regarding the output.”
• Extreme learning machines: a survey (2011, methodology): “[82] presented a pruned ELM (referred to as P-ELM) as a systematic and automated method for ELM classifier network design.”
• Extreme learning machine: algorithm, theory and applications (2013, background): “Rong et al. (2008) address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to under-fitting/over-fitting issues in pattern classification.”