NDLI: Contemporary Classification on Medical Data based on Non-Linear Feature Extraction

Content Provider	IEEE Xplore Digital Library
Author	Aribarg, T. Supratid, S. Lursinsap, C.
Copyright Year	2009
Abstract	High dimensional data in several applications seriously spoils classification computation of several types of learning. In order to relieve the difficulties of such a high dimension, this paper proposes the classification computation, which refers to a modified neural network: the neural network with weights optimized by particle swarm intelligence. The contemporary is placed on the combination of the non-linear feature extraction and such a classification method. 10-fold cross-validation experiments of each method are performed on five medical data sets. The results indicate not only the improvement of classification based on non-linear feature extraction, but also indicate the reduction of the number of features for classification.
Starting Page	17
Ending Page	23
File Size	256666
Page Count	7
File Format	PDF
ISBN	9780769537016
DOI	10.1109/ICCSA.2009.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-29
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Principal component analysis Particle swarm optimization Neural networks Multi-layer neural network Kernel Adaptive systems Breast cancer Medical diagnostic imaging Intelligent networks particale swarm optimization kernel principal component non-linear feature extraction neural network adaptive neuro-fuzzy inference system
Content Type	Text
Resource Type	Article

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