Causality‐based online streaming feature selection. (11th May 2021)

• Record Type: Journal Article
• Authors: Li, Longzhu; Lin, Yaojin; Zhao, Hong; Chen, Jinkun; Li, Shaozi
• Is Part Of: Concurrency and computation. Volume 33:Number 20(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-11
• Subjects: Bayesian network -- causal relationship -- Markov blanket discovery -- neighborhood conditional mutual information -- online streaming feature selection; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6347
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Online streaming feature selection, as a well‐known and effective preprocessing approach in machine learning, is an eternal topic. Amount of online streaming feature selection algorithms have achieved a great deal of success in classification and prediction tasks. However, most of these existing algorithms only concentrate on the relevance between features and labels, and neglect the causal relationships between them. Discovering the potential causal relationships between features and labels, that is, the Markov blanket (MB) of class label, which can build a more interpretable and robust classification model. In this paper, we put forward a causality‐based online streaming feature selection algorithm with neighborhood conditional mutual information. First, we apply neighborhood symmetrical uncertainty to discover a candidate Markov blanket (CMB) with causal information. Then, neighborhood conditional mutual information instead of conditional independence test is used to delete the false positives in CMB, which can significantly alleviate the computational cost. Moreover, we utilize the updated CMB to choose the true spouses, which may be mistakenly deleted during the process of removing false positives, and then acquire an optimal MB as the online selected feature subset. Finally, causality‐based online streaming feature selection with neighborhood conditional mutual information is compared with four well‐established online streaming feature selection methods on 13 real‐world datasets. Experiment results show that the proposed algorithm outperforms these online streaming feature selection algorithms.