Novel hybrid feature selection algorithms for diagnosing erythemato-squamous diseases

Authors:
Juanying Xie;Jinhu Lei;Weixin Xie;Xinbo Gao;Yong Shi;Xiaohui Liu
Affiliations:
School of computer science, Shaanxi Normal University, Xi'an, China and School of Electronic Engineering, Xidian University, Xi'an, China;School of computer science, Shaanxi Normal University, Xi'an, China;School of Electronic Engineering, Xidian University, Xi'an, China and School of Information Engineering, Shenzhen University, Shenzhen, China;School of Electronic Engineering, Xidian University, Xi'an, China;CAS Research Centre of Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China;School of Information systems, Computing and Mathematics, Brunel University, London, UK
Venue:
HIS'12 Proceedings of the First international conference on Health Information Science
Year:
2012

Citing 15
Cited 0

Floating search methods in feature selection

Pattern Recognition Letters
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
An introduction to variable and feature selection

The Journal of Machine Learning Research
The effect to diagnostic accuracy of decision tree classifier of fuzzy and k-NN based weighted pre-processing methods to diagnosis of erythemato-squamous diseases

Digital Signal Processing
Similarity classifier using similarity measure derived from Yu's norms in classification of medical data sets

Computers in Biology and Medicine
A Direct Method of Nonparametric Measurement Selection

IEEE Transactions on Computers
Multiclass support vector machines for diagnosis of erythemato-squamous diseases

Expert Systems with Applications: An International Journal
A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems

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
A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases

Expert Systems with Applications: An International Journal
Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering

Journal of Medical Systems
Letters: An ensemble of classifiers for the diagnosis of erythemato-squamous diseases

Neurocomputing
Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes hybrid feature selection algorithms to build the efficient diagnostic models based on a new accuracy criterion, generalized F-score (GF) and SVM. The hybrid algorithms adopt Sequential Forward Search (SFS), and Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, with SVM to accomplish hybrid feature selection with the new accuracy criterion to guide the procedure. We call them as modified GFSFS, GFSFFS and GFSBFS, respectively. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the efficient classifiers. To get the best and statistically meaningful classifiers, we not only conduct 10-fold cross validation experiments on training subset, but also on the whole erythemato-squamous diseases datasets. Experimental results show that our proposed hybrid methods construct efficient diagnosis classifiers with high average accuracy when compared with traditional algorithms.