Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.
References
[1]
Pawlak, Z. (1982) Rough Sets. International Journal of Computer and Information Sciences, 11, 341-356. https://doi.org/10.1007/BF01001956
[2]
Ju, H.R., et al. (2017) Cost-Sensitive Rough Set: A Multi-Granulation Approach. Knowledge-Based Systems, 123, 137-153. https://doi.org/10.1016/j.knosys.2017.02.019
[3]
Wang, G.Y., Yao, Y.Y. Yu, H. (2009) A review of Rough Set Theory and Its Applications. Journal of Computer Research, 32, 1229-1246. https://doi.org/10.3724/SP.J.1016.2009.01229
[4]
Hu, Q.H., Yu, D.R. Xie, Z.X. (2008) Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. Journal of Software, 19, 640-649. https://doi.org/10.3724/SP.J.1001.2008.00640
[5]
Liu, Y., et al. (2014) Quick Attribute Reduction Algorithm for Neighborhood Rough Set Model. Information Sciences, 271, 65-81. https://doi.org/10.1016/j.ins.2013.08.022
[6]
Yao, Y.Y. (2010) Three-Way Decisions with Probabilistic Rough Sets. Information Sciences, 180, 341-353. https://doi.org/10.1016/j.ins.2009.09.021
[7]
Pawlak, Z., Wong, S.K.M. Ziarko, W. (1988) Rough Sets: Probabilistic Versus Deterministic Approach. International Journal of Man-Machine Studies, 29, 81-95. https://doi.org/10.1016/S0020-7373(88)80032-4
[8]
Yao, Y.Y. (2015) Rough Sets and Three-Way Decisions. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-319-25754-9_6
[9]
Ju, H.R., Li, H.X., Zhou, X.Z., et al. (2017) A Sequential Three-Decision Classifier Based on Local Attributes. Computer Science, 44, 34-39 57.
[10]
Hu, P., Qin, L.X. Yao, H.M. (2016) Automatic Determination of Three-Decision Thresholds Using Artificial Fish Swarm Algorithm. Computer Science and Modemization, 6, 97-102.
[11]
Sheng, R.H., Li, H.Y., Jiang, C.M., et al. (2021) Research on an Attribute Reduction Method Based on Sequential Three-Decision Classification. Fuzzy Systems and Mathematics, 35, 48-65.
[12]
Jiang, C.M. Liu, A.P. (2020) An Accelerated Attribute Reduction Method from the Perspective of Trilateral Decisions. Computer Engineering and Science, 42, 2280-2286.
