Abstract
A feature selection technique is proposed in this paper, which combines the computational ease of filters and the performance superiority of wrappers. The technique sequentially combines Fisher-score-based ranking and logistic regression-based wrapping. On synthetically generated data, the 5-fold cross-validation performances of the proposed technique were compatible with the performances achieved through Least Absolute Shrinkage and Selection Operator (LASSO). The binary classification performances in terms of F1 score and Geometric Mean (GM) were evaluated over a varying imbalance ratio of 0.1:0.9 – 0.5:0.5, a number of informative features of 1 – 30, and a fixed sample size of 5000.
Publisher
Engineering, Technology & Applied Science Research
Reference14 articles.
1. I. Guyon and A. Elisseeff, "An Introduction to Variable and Feature Selection," Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.
2. G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning with Applications in R, New York, NY, USA: Springer.
3. S. Nuanmeesri and W. Sriurai, "Thai Water Buffalo Disease Analysis with the Application of Feature Selection Technique and Multi-Layer Perceptron Neural Network," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6907–6911, Apr. 2021.
4. S. Matharaarachchi, M. Domaratzki, and S. Muthukumarana, "Assessing feature selection method performance with class imbalance data," Machine Learning with Applications, vol. 6, Dec. 2021, Art. no. 100170.
5. D. K. Singh and M. Shrivastava, "Evolutionary Algorithm-based Feature Selection for an Intrusion Detection System," Engineering, Technology & Applied Science Research, vol. 11, no. 3, pp. 7130–7134, Jun. 2021.