Abstract
For binary classification, the three-way decision divides samples into positive (POS) region, negative (NEG) region, and boundary region (BND). The correct division of these boundary data is helpful to improve the accuracy of binary classification. However, how to construct the optimal feature representation from certain samples for boundary domain partition is a challenge. In this paper, we propose attention enhanced hierarchical feature representation for three-way decision boundary processing (AHT) to deal with the boundary region. Based on the three-way decision, certain regions (positive, negative) and boundary regions are obtained. Obtaining the hierarchical feature representations on the positive domain and the negative domain respectively. Constructing attention-enhanced fusion feature representation to guide the boundary domain division of the testing set. The experimental results on five UCI datasets show that our algorithm effectively improves binary classification accuracy.
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Chen, J., Chen, Y., Xu, Y., Zhao, S., Zhang, Y. (2021). Attention Enhanced Hierarchical Feature Representation for Three-Way Decision Boundary Processing. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_18
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