Abstract
A two-phase classification method is proposed based on three-way decisions. In the first phase, all objects are classified into three different regions by three-way decisions. A positive rule makes a decision of acceptance, a negative rule makes a decision of rejection, and a boundary rule makes a decision of abstaining. The positive region contains those objects that have been assigned a class label with a high level of confidence. The boundary and negative regions contain those objects that have not been assigned class labels. In the second phase, a simple ensemble learning approach to determine the class labels of objects in the boundary or negative regions. Experiments are performed to compare the proposed two-phase classification approach and a classical classification approach. The results show that our method can produce a better classification accuracy than the classical model.
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Li, W., Huang, Z., Jia, X. (2013). Two-Phase Classification Based on Three-Way Decisions. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_32
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DOI: https://doi.org/10.1007/978-3-642-41299-8_32
Publisher Name: Springer, Berlin, Heidelberg
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