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- W2745409620 abstract "The use of constraint optimization has recently proven to be a successful approach to providing solutions to various NP-hard search and optimization problems in data analysis. In this work we extend the use of constraint optimization systems further within data analysis to a central problem arising from the analysis of multivariate data, namely, determining minimum-width multivariate confidence intervals, i.e., the minimum-width confidence band problem (MWCB). Pointing out drawbacks in recently proposed formalizations of variants of MWCB, we propose a new problem formalization which generalizes the earlier formulations and allows for circumvention of their drawbacks. We present two constraint models for the new problem in terms of mixed integer programming and maximum satisfiability, as well as a greedy approach. Furthermore, we empirically evaluate the scalability of the constraint optimization approaches and solution quality compared to the greedy approach on real-world datasets." @default.
- W2745409620 created "2017-08-31" @default.
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- W2745409620 date "2017-01-01" @default.
- W2745409620 modified "2023-09-23" @default.
- W2745409620 title "Minimum-Width Confidence Bands via Constraint Optimization" @default.
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- W2745409620 doi "https://doi.org/10.1007/978-3-319-66158-2_29" @default.
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