Paper Info
Title
Artificial Neural Network Based On Rotation Forest For Biomedical Pattern Classification
Abstract
The novel classifier system based on ensemble classifier is proposed in this paper. Rotation forest algorithm based on principal component algorithm was used as ensemble classifier method. In presented classifier system, artificial neural network was used as base classifier in this ensemble classifier system. Rotation forest structure has been generally realized with decision trees in literature. But, multilayer perceptron neural network was utilized as base classifier in rotation forest structure in our study. However, principal component analysis was used for obtaining different feature sets from original data set. The proposed RF-ANN structure was applied to Wisconsin breast cancer data taken form UCI Database. The obtained results were compared with the results of neural network optimized particle swarm optimization (PSO-ANN). The realized experimental studies were represented that RF-ANN structure was successful than PSO-ANN structure. RF-ANN classified breast cancer dataset with 98.05% classification accuracy using 9 classifiers.
Year
DOI
Venue
2013
10.1109/TSP.2013.6614001
2013 36TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP)
Keywords
Field
DocType
Artificial neural network, biomedical pattern classification, classifier ensembles, rotation forest
Particle swarm optimization,Decision tree,Data mining,Pattern recognition,Computer science,Rotation forest,Artificial intelligence,Margin classifier,Classifier (linguistics),Artificial neural network,Principal component analysis,Quadratic classifier
Conference
Citations 
PageRank 
References 
4
0.40
10
Authors
2
Name
Order
Citations
PageRank
Hasen Koyuncu1144.84
Rahime Ceylan225917.10