Name
Title | ||
|---|---|---|
A new feature selection method on classification of medical datasets: Kernel F-score feature selection |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2009.01.041 | Expert Syst. Appl. |
Keywords | Field | DocType |
f-score feature selection,least square support vector machine (ls-svm),heart disease dataset,high dimensional input feature,medical datasets,high dimension feature space,proposed feature selection method,linearly separable feature space,feature space,levenberg–marquardt artificial neural network,high dimensional feature space,feature selection,kernel f-score feature selection,new feature selection method,spect images dataset,escherichia coli promoter gene sequence dataset,kernel function,radial basis function,least squares support vector machine,escherichia coli,machine learning,levenberg marquardt,artificial neural network | Graph kernel,Data mining,Dimensionality reduction,Feature selection,Radial basis function kernel,Computer science,Artificial intelligence,k-nearest neighbors algorithm,Feature vector,Pattern recognition,Feature (computer vision),Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 7 | Expert Systems With Applications |
Citations | PageRank | References |
41 | 1.48 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Polat | 1 | 1348 | 97.38 |
| Salih Güneş | 2 | 1267 | 78.53 |