We consider bi-criteria optimization problems for decision rules and rule systems relative to length and coverage. We study decision tables with many-valued decisions in which each row is associated with a set of decisions as well as single-valued decisions where each row has a single decision. Short rules are more understandable; rules covering more rows are more general. Both of these problems—minimization of length and maximization of coverage of rules are NP-hard. We create dynamic programming algorithms which can find the minimum length and the maximum coverage of rules, and can construct the set of Pareto optimal points for the corresponding bi-criteria optimization problem. This approach is applicable for medium-sized decision tables. However, the considered approach allows us to evaluate the quality of various heuristics for decision rule construction which are applicable for relatively big datasets. We can evaluate these heuristics from the point of view of (i) single-criterion—we can compare the length or coverage of rules constructed by heuristics; and (ii) bi-criteria—we can measure the distance of a point (length, coverage) corresponding to a heuristic from the set of Pareto optimal points. The presented results show that the best heuristics from the point of view of bi-criteria optimization are not always the best ones from the point of view of single-criterion optimization.
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Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). We are greatly indebted to the anonymous reviewer for useful comments and suggestions.
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Alsolami, F., Amin, T., Chikalov, I. et al. Bi-criteria optimization problems for decision rules. Ann Oper Res 271, 279–295 (2018). https://doi.org/10.1007/s10479-018-2905-0
- Decision tables with many-valued decisions
- Systems of decision rules
- Dynamic programming
- Pareto optimal points
- Greedy heuristics