PSO-Based Attribute Reduction of Rough Set and Its Application

Title:PSO-Based Attribute Reduction of Rough Set and Its Application

Volume: 10 Issue: 4

Author(s): Jianchuan Bai, Kewen Xia*, Baokai Zu and Panpan Wu

Affiliation:

• School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China and Key Lab of Big Data Computation of Hebei Province, 5340 Xiping Road, Tianjin 300401,China

Keywords: Rough set, particle swarm optimization, logging data, SOCP-RVM, RVM, data mining.

Abstract: Background: The rough set theory is a powerful tool to deal with imprecise and incomplete information in the field of data mining. As a core content for rough set theory, attribute reduction aims at removing redundant data and drawing the minimum attributes while maintaining indiscernibility relation. However, traditional rough set theory is available for classical example which has disadvantages of time-consumption, large storage and low recognition accuracy. In this paper, we focus on an attribute reduction based on particle swarm optimization (PSO) to overcome the drawbacks of traditional rough set theory. Firstly, this paper reviews some important concepts of rough set and particle swarm optimization. Then, we establish the model of attribute reduction based on particle swarm optimization. Finally, the proposed method is applied to actual oil logging data, and the reduction results are recognized by Relevance Vector Machine (RVM) and Second Order Cone Programming-Relevance Vector Machine(SOCP-RVM). The experimental results show that the proposed method is efficient and has high recognition accuracy.

Methods: Recent publications and patent databases are reviewed to find extraordinary and innovative attribute reduction algorithms for reducing time consumption and accuracy.

Results: Two methods which are RVM and SOCP-RVM are applied to recognize the attribute reduction results. The results show that well in 993-997m, 1045-1152.5m and 1236-1255m depth are main oil-layers, the rest are dry-layers (Fig. 2 shows that oil-layers are 995-997m, 1045-1152.5m and 1241.5-1255m; Fig. (3) shows that oil-layers are 993-996m, 1055.5-1143m and 1236-1251m). The recognition results are consistent with the actual oil test results.

Conclusion: A novel PSO-Based Attribute Reduction of Rough Set is proposed, and apply it to oil well to deal with actual and complex data. Experimental results show that the proposed algorithm can get effective reduction sets, and the recognition results with high accuracy can be obtained in actual well by using RVM and SOCP-RVM algorithms, which are consistent with the actual oil conclusion. It indicates that the proposed attribute reduction based on PSO is practical and viable, and the reduction results are efficient.

Cite this article as:

Bai Jianchuan , Xia Kewen *, Zu Baokai and Wu Panpan, PSO-Based Attribute Reduction of Rough Set and Its Application, Recent Patents on Computer Science 2017; 10 (4) . https://dx.doi.org/10.2174/2213275911666180216153458