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- Title
Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification.
- Authors
Bodur, Ersin Kuset; Atsa'am, Donald Douglas
- Abstract
This research developed and tested a filter algorithm that serves to reduce the feature space in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates the risk ratio of each predictor with the response, and outputs ratios that represent the association between a predictor and the class attribute. The value of the association translates to the importance rank of the corresponding predictor in determining the outcome. Using Random Forest and Logistic regression classification, the performance of the developed algorithm was compared against the regsubsets and varImp functions, which are unsupervised methods of variable selection. Equally, the proposed algorithm was compared with the supervised Fisher score and Pearson's correlation feature selection methods. Different datasets were used for the experiment, and, in the majority of the cases, the predictors selected by the new algorithm outperformed those selected by the existing algorithms. The proposed filter algorithm is therefore a reliable alternative for variable ranking in data mining classification tasks with a dichotomous response.
- Subjects
DIMENSION reduction (Statistics); DATA reduction; RANDOM forest algorithms; DATA mining; FEATURE selection; ALGORITHMS; FILTERS &; filtration
- Publication
Processes, 2019, Vol 7, Issue 4, p222
- ISSN
2227-9717
- Publication type
Article
- DOI
10.3390/pr7040222