Applied Intelligence

, Volume 50, Issue 1, pp 101–118 | Cite as

Feature selection with Symmetrical Complementary Coefficient for quantifying feature interactions

  • Rui Zhang
  • Zuoquan ZhangEmail author


In the field of machine learning and data mining, feature interaction is a ubiquitous issue that cannot be ignored and has attracted more attention in recent years. In this paper, we proposed the Symmetrical Complementary Coefficient which can quantify feature interactions very well. Based on it, we improved the Sequential Forward Selection (SFS) algorithm and proposed a new feature subset searching algorithm called SCom-SFS which only needs to consider the feature interactions between adjacent features on a given sequence instead of all of them. Moreover, discovered feature interactions can speed up the process of searching for the optimal feature subset. In addition, we have improved the ReliefF algorithm by screening out representative samples from the original data set, and need not to sample the samples. The improved ReliefF algorithm has been proved to be more efficient and reliable. An effective and complete feature selection algorithm RRSS is obtained through the combination of the two modified algorithms. According to the experimental results, the proposed algorithm RRSS outperformed five classic and two latest feature selection algorithms in terms of size of resulting feature subset, Accuracy, Kappa coefficient, and adjusted Mean-Square Error (MSE).


Feature selection ReliefF Sequential Forward Selection Feature interaction Random Forest Symmetrical Complementary Coefficient 



Thanks to the data sets provided by the UCI repository. And The breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. The Statlog-Vehicle data set was from the Turing Institute, Glasgow, Scotland. Also thanks to R language and the authors of different packages.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ScienceBeijing Jiaotong UniversityBeijingChina

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