Modeling spectral data based on mutual information and kernel extreme learning machines

Authors:
Li-Jie Zhao;Jian Tang;Tian-you Chai
Affiliations:
College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China,Research Center of Automation, Northeastern University, Shenyang, China;Unit 92941, PLA, Huludao, China,Research Center of Automation, Northeastern University, Shenyang, China;Research Center of Automation, Northeastern University, Shenyang, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

Citing 7
Cited 0

Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Convex incremental extreme learning machine

Neurocomputing
Enhanced random search based incremental extreme learning machine

Neurocomputing
Feature Selection with Kernel Class Separability

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection with dynamic mutual information

Pattern Recognition
Universal approximation using incremental constructive feedforward networks with random hidden nodes

IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Effective modeling based on the high dimensional data needs feature selection and fast learning speed. Aim at this problem, a novel modeling approach based on mutual information and extreme learning machines is proposed in this paper. Simple mutual information based feature selection method is integrated with the fast learning kernel based extreme learning machines to obtain better modeling performance. In the method, optimal number of the features and learning parameters of models are selected simultaneously. The simulation results based on the near-infrared spectrum show that the proposed approach has better prediction performance and fast leaning speed.