The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Selecting salient features for classification based on neural network committees
Pattern Recognition Letters
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Feature selection in a kernel space
Proceedings of the 24th international conference on Machine learning
Computer Methods and Programs in Biomedicine
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Dual-population based coevolutionary algorithm for designing RBFNN with feature selection
Expert Systems with Applications: An International Journal
International Journal of Computational Intelligence Studies
Expert Systems with Applications: An International Journal
General framework for class-specific feature selection
Expert Systems with Applications: An International Journal
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
Predicting seminal quality with artificial intelligence methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg-Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.