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Floating search and conditional independence testingfor causal feature selection |
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| รหัสดีโอไอ | |
| Creator | 1. Rakkrit Duangsoithong 2. Yuying Zhao |
| Title | Floating search and conditional independence testingfor causal feature selection |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2564 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 43 |
| Journal No. | 6 |
| Page no. | 1823-1830 |
| Keyword | causal feature selection, floating search, redundant features, conditional independence testing, classification accuracy |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | The curse of dimensionality and over-fitting problems are usually associated with high-dimensional data. Featureselection is one method that can overcome these problems. This paper proposes floating search and conditional independencetesting as a causal feature selection algorithm (FSCI). FSCI uses mutual information with floating search strategy to eliminateirrelevant features and removes redundant features using conditional independence testing. The experimental demonstration isbased on 8 datasets and the results are evaluated by number of selected features, classification accuracy, and complexity of thealgorithm. The results are compared with the non-causal feature selection algorithms FCBF, ReliefF, and with the causal featureselection algorithms MMPC, IAMB, FBED and MMMB. The overall results show that the average number of features selectedby the proposed FSCI algorithm (12.8) is below those with ReliefF (16.5) and MMMB (13) algorithms. According to theclassification tests, FSCI algorithm provided the highest average accuracy (87.40%) among the feature selection methods tested.Moreover, FSCI can infer causality with less complexity. |
