Fast Relevant Feature Selection Based on Markov Boundary Learning from Data Stream for Classification Tasks

Author:

Prince Dukundane Gashongore1,Bou Savong2

Affiliation:

1. Graduate School of Systems and Information Engineering, University of Tsukuba

2. Center for Computational Sciences, University of Tsukuba

Publisher

Information Processing Society of Japan

Subject

General Computer Science

Reference19 articles.

1. [1] Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1988).

2. [2] Tsamardinos, I. and Aliferis, C.F.: Towards principled feature selection: Relevancy, filters and wrappers, International Workshop on Artificial Intelligence and Statistics, PMLR, pp.300-307 (2003).

3. [3] Yu, K., Liu, L. and Li, J.: A unified view of causal and non-causal feature selection, ACM Trans. Knowledge Discovery from Data (TKDD), Vol.15, No.4, pp.1-46 (2021).

4. [4] Gao, T. and Ji, Q.: Local Causal Discovery of Direct Causes and Effects, Advances in Neural Information Processing Systems, Cortes, C., Lawrence, N., Lee, D., Sugiyama, M. and Garnett, R. (Eds.), Curran Associates, Inc. (2015).

5. [5] Yu, K., Guo, X., Liu, L., Li, J., Wang, H., Ling, Z. and Wu, X.: Causality-based feature selection: Methods and evaluations, ACM Computing Surveys (CSUR), Vol.53, No.5, pp.1-36 (2020).

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