The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines for Text Categorization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Face Pose Discrimination Using Support Vector Machines (SVM)
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
An introduction to variable and feature selection
The Journal of Machine Learning Research
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Computers in Biology and Medicine
Adaptive branch and bound algorithm for selecting optimal features
Pattern Recognition Letters
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Combined neural networks for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Feature selection with dynamic mutual information
Pattern Recognition
The search for optimal feature set in power quality event classification
Expert Systems with Applications: An International Journal
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An evaluation of filter and wrapper methods for feature selection in categorical clustering
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases
HIS'12 Proceedings of the First international conference on Health Information Science
An expert system for optimising thyroid disease diagnosis
International Journal of Computational Science and Engineering
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, we developed a diagnosis model based on support vector machines (SVM) with a novel hybrid feature selection method to diagnose erythemato-squamous diseases. Our proposed hybrid feature selection method, named improved F-score and Sequential Forward Search (IFSFS), combines the advantages of filter and wrapper methods to select the optimal feature subset from the original feature set. In our IFSFS, we improved the original F-score from measuring the discrimination of two sets of real numbers to measuring the discrimination between more than two sets of real numbers. The improved F-score and Sequential Forward Search (SFS) are combined to find the optimal feature subset in the process of feature selection, where, the improved F-score is an evaluation criterion of filter method, and SFS is an evaluation system of wrapper method. The best parameters of kernel function of SVM are found out by grid search technique. Experiments have been conducted on different training-test partitions of the erythemato-squamous diseases dataset taken from UCI (University of California Irvine) machine learning database. Our experimental results show that the proposed SVM-based model with IFSFS achieves 98.61% classification accuracy and contains 21 features. With these results, we conclude our method is very promising compared to the previously reported results.