Multi-Relational Naïve Bayesian Classification Based on the Selection of Relations

Abstract:

Nowadays, multi-relational classification has become a hotspot for research and application in the field of data mining. Compared to the single table with simple structure, multi-relational tables is more complicated. However, not all of the information in the tables has good effects on classification. It may decrease the classification accuracy of the algorithm when irrelevant relations are added. In this article, we optimized the multi-relational tables using the usefulness of the backgrounds to remove those relations which have little effect on the classification. The results show that, this method is effective.

• Periodical: Advanced Materials Research (Volumes 1070-1072)
• Pages: 2066-2072
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1070-1072.2066
• Online since: December 2014
• Authors: Jing Zhang, Jia Jia Bi, Ning Sun, Xue Gang Hu
• Keywords: Classification, Data Mining, Multi-Relational Mining, Relation Selection

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