A fast pruned-extreme learning machine for classification problem

Authors:
Hai-Jun Rong;Yew-Soon Ong;Ah-Hwee Tan;Zexuan Zhu
Affiliations:
Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Venue:
Neurocomputing
Year:
2008

Citing 9
Cited 19

A sequential learning scheme for function approximation using minimal radial basis function neural networks

Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Letters: Convex incremental extreme learning machine

Neurocomputing
Extreme learning machine for predicting HLA-Peptide binding

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions

IEEE Transactions on Neural Networks
Learning capability and storage capacity of two-hidden-layer feedforward networks

IEEE Transactions on Neural Networks
Real-time learning capability of neural networks

IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes

IEEE Transactions on Neural Networks

Partial Lanczos extreme learning machine for single-output regression problems

Neurocomputing
OP-ELM: optimally pruned extreme learning machine

IEEE Transactions on Neural Networks
Two-stage extreme learning machine for regression

Neurocomputing
Constructive hidden nodes selection of extreme learning machine for regression

Neurocomputing
An intelligent fast sales forecasting model for fashion products

Expert Systems with Applications: An International Journal
On the performance of the µ-GA extreme learning machines in regression problems

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Letters: Regularized extreme learning machine for regression problems

Neurocomputing
A rank reduced matrix method in extreme learning machine

ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction

Knowledge-Based Systems
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis

Neural Processing Letters
Extending extreme learning machine with combination layer

IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Bankruptcy prediction using Extreme Learning Machine and financial expertise

Neurocomputing
Constructive multi-output extreme learning machine with application to large tanker motion dynamics identification

Neurocomputing
Hierarchical extreme learning machine for feedforward neural network

Neurocomputing
Aircraft recognition using modular extreme learning machine

Neurocomputing
Extreme learning machines for soybean classification in remote sensing hyperspectral images

Neurocomputing
Ensemble delta test-extreme learning machine (DT-ELM) for regression

Neurocomputing
Hybrid extreme rotation forest

Neural Networks
Fast fashion sales forecasting with limited data and time

Decision Support Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.