Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10367094 | Information and Software Technology | 2013 | 16 Pages |
Abstract
Local bias in cross-company data does harm model calibration and adds noisy factors to model maintenance. The proposed local bias measure offers a means to quantify degree of local bias associated with a cross-company dataset, and assess its influence on parametric model performance. The local bias based weighted sampling technique can be applied to trade-off and mitigate potential risk of significant local bias, which limits the usability of cross-company data for general parametric model calibration and maintenance.
Related Topics
Physical Sciences and Engineering
Computer Science
Human-Computer Interaction
Authors
Ye Yang, Zhimin He, Ke Mao, Qi Li, Vu Nguyen, Barry Boehm, Ricardo Valerdi,