Causal Feature Selection With Dual Correction
| العنوان: | Causal Feature Selection With Dual Correction |
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| المؤلفون: | Xianjie Guo, Kui Yu, Lin Liu, Fuyuan Cao, Jiuyong Li |
| المساهمون: | Guo, Xianjie, Yu, Kui, Liu, Lin, Cao, Fuyuan, Li, Jiuyong |
| المصدر: | IEEE Transactions on Neural Networks and Learning Systems. :1-14 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | feature selection, Artificial Intelligence, Computer Networks and Communications, dual correction, Bayesian network (BN), Markov boundary (MB), Software, Computer Science Applications |
| الوصف: | usc Causal feature selection methods aim to identify a Markov boundary (MB) of a class variable, and almost all the existing causal feature selection algorithms use conditional independence (CI) tests to learn the MB. However, in real-world applications, due to data issues (e.g., noisy or small samples), CI tests can be unreliable; thus, causal feature selection algorithms relying on CI tests encounter two types of errors: false positives (i.e., selecting false MB features) and false negatives (i.e., discarding true MB features). Existing algorithms only tackle either false positives or false negatives, and they cannot deal with both types of errors at the same time, leading to unsatisfactory results. To address this issue, we propose a dual-correction-strategy-based MB learning (DCMB) algorithm to correct the two types of errors simultaneously. Specifically, DCMB selectively removes false positives from the MB features currently selected, while selectively retrieving false negatives from the features currently discarded. To automatically determine the optimal number of selected features for the selective removal and retrieval in the dual correction strategy, we design the simulated-annealing-based DCMB (SA-DCMB) algorithm. Using benchmark Bayesian network (BN) datasets, the experimental results demonstrate that DCMB achieves substantial improvements on the MB learning accuracy compared with the existing MB learning methods. Empirical studies in real-world datasets validate the effectiveness of SA-DCMB for classification against state-of-the-art causal and traditional feature selection algorithms. Refereed/Peer-reviewed |
| تدمد: | 2162-2388 2162-237X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75f7799e2907bdb404382c15092b278b https://doi.org/10.1109/tnnls.2022.3178075 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....75f7799e2907bdb404382c15092b278b |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21622388 2162237X |
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