Abstract
As for the problem of subjectivity and lack of scientificity in performance evaluation, the paper introduces a RPC model with cube Bézier curve being principal curve, and designs the learning algorithm of Bézier curve. First, evaluation indicators are chosen using Pearson correlation coefficient, RPC two-dimensional projection is conducted on relevant data and the ranking result of multiple indicator observation data is obtained. The experiment is done with the performance data of 12345 hotline telephone operators in one month of 2019 in one district of Beijing, and the ranking result is in line with the popular perception. The experimental result proves that as for the ranking in performance evaluation, RPC method is more scientific and interpretable than the method of weighted average and Elmap model.
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Acknowledgements
This work is supported by National Key R&D Program of China (Grant number 2018YFC0704800), Natural Science Foundation of China (No: 41971366, 91846301), the 2018–2019 Excellent Talents Program in Xicheng District of Beijing and Beijing Urban Governance Research Center.
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Jin, W., Guo, D., Zhao, Lk., Li, JC. (2021). Performance Ranking Based on Bézier Ranking Principal Curve. In: Meng, X., Xie, X., Yue, Y., Ding, Z. (eds) Spatial Data and Intelligence. SpatialDI 2020. Lecture Notes in Computer Science(), vol 12567. Springer, Cham. https://doi.org/10.1007/978-3-030-69873-7_15
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