A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Genetic selection and neural modeling for designing pattern classifiers
Genetic selection and neural modeling for designing pattern classifiers
Independent component analysis: algorithms and applications
Neural Networks
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Classifier Design by Linear Programming
IEEE Transactions on Computers
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
An expert system based on wavelet decomposition and neural network for modeling Chua's circuit
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Image segmentation using association rule features
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Association rule-based feature selection method for Alzheimer's disease diagnosis
Expert Systems with Applications: An International Journal
Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
HIS'12 Proceedings of the First international conference on Health Information Science
Functional brain image classification using association rules defined over discriminant regions
Pattern Recognition Letters
Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules
Engineering Applications of Artificial Intelligence
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In this paper, a new feature selection method based on Association Rules (AR) and Neural Network (NN) is presented for the diagnosis of erythemato-squamous diseases. AR is used for reducing the dimension of erythemato-squamous diseases dataset and NN is used for efficient classification. The proposed AR+NN system performance is compared with that of other feature selection algorithms+NN. The dimension of input feature space is reduced from thirty four to twenty four by using AR. In test stage, 3-fold cross validation method is applied to the erythemato-squamous diseases dataset to evaluate the proposed system performances. The correct classification rate of proposed system is 98.61%. This research demonstrated that the AR can be used for reducing the dimension of feature space and proposed AR+NN model can be used to obtain fast automatic diagnostic systems for other diseases.