New feature selection based on kernel

Zuherman Rustam, Sri Hartini

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Feature selection is an essential issue in machine learning. It discards the unnecessary or redundant features in the dataset. This paper introduced the new feature selection based on kernel function using 16 the real-world datasets from UCI data repository, and k-means clustering was utilized as the classifier using radial basis function (RBF) and polynomial kernel function. After sorting the features using the new feature selection, 75 percent of it was examined and evaluated using 10-fold cross-validation, then the accuracy, F1-Score, and running time were compared. From the experiments, it was concluded that the performance of the new feature selection based on RBF kernel function varied according to the value of the kernel parameter, opposite with the polynomial kernel function. Moreover, the new feature selection based on RBF has a faster running time compared to the polynomial kernel function. Besides, the proposed method has higher accuracy and F1-Score until 40 percent difference in several datasets compared to the commonly used feature selection techniques such as Fisher score, Chi-Square test, and Laplacian score. Therefore, this method can be considered to use for feature selection.

Original languageEnglish
Pages (from-to)1569-1577
Number of pages9
JournalBulletin of Electrical Engineering and Informatics
Issue number4
Publication statusPublished - Aug 2020


  • Feature selection
  • K-means clustering
  • Kernel
  • Kernel matrix
  • Polynomial kernel
  • Radial basis function kernel


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