Name
Title | ||
|---|---|---|
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost |
Abstract | ||
|---|---|---|
In several applications of automatic diagnosis and active learning, a central problem is the evaluation of a discrete function by adaptively querying the values of its variables until the values read uniquely determine the value of the function. In general, the process of reading the value of a variable might involve some cost. This cost should be taken into account when deciding the next variable to read. The goal is to design a strategy for evaluating the function incurring little cost (in the worst case or in expectation according to a prior distribution on the possible variables’ assignments). Our algorithm builds a strategy (decision tree) which attains a logarithmic approximation simultaneously for the expected and worst cost spent. This is best possible under the assumption that . |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s00453-016-0225-9 | Algorithmica |
Keywords | Field | DocType |
Decision tress,Approximation algorithms,Hardness of approximation,Function evaluation | Decision tree,Mathematical optimization,Combinatorics,Active learning,Logarithm,Expected cost,Simultaneous optimization,Prior probability,Mathematics | Journal |
Volume | Issue | Citations |
79 | 3 | 2 |
PageRank | References | Authors |
0.43 | 28 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ferdinando Cicalese | 1 | 450 | 48.20 |
| Eduardo Sany Laber | 2 | 229 | 27.12 |
| Aline Medeiros Saettler | 3 | 18 | 4.17 |