A Fast Hybrid Feature Selection Method
Published in IEEE 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2020
Recommended citation: M. A. Ganjei and R. Boostani, "A Fast Hybrid Feature Selection Method," 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 6-11, doi: 10.1109/ICCKE48569.2019.8964884. https://ieeexplore.ieee.org/document/8964884
In this paper, we proposed a new hybrid feature selection method, in which in the filter stage the features are ranked according to their relevance. Instead of running the wrapper on all the features, we use a split-to-blocks technique and show that block size has a considerable impact on performance. A sequential forward selection (SFS) method was applied to the ranked blocks of features in order to find the most relevant features. The proposed method rapidly eliminates a large number of irrelevant features in its ranking stage, and then different block sizes were evaluated in the wrapper phase by choosing a proper block size using SFS. It causes this method to have a low time complexity, despite the good results.
Recommended citation: M. A. Ganjei and R. Boostani, “A Fast Hybrid Feature Selection Method,” 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 2019, pp. 6-11, doi: 10.1109/ICCKE48569.2019.8964884.