Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis
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ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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Date
2017-10-19Online Publication Date
2017-10-19Print Publication Date
2018-04Permanent link to this record
http://hdl.handle.net/10754/625914
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This paper is devoted to the study of bi-criteria optimization problems for decision trees. We consider different cost functions such as depth, average depth, and number of nodes. We design algorithms that allow us to construct the set of Pareto optimal points (POPs) for a given decision table and the corresponding bi-criteria optimization problem. These algorithms are suitable for investigation of medium-sized decision tables. We discuss three examples of applications of the created tools: the study of relationships among depth, average depth and number of nodes for decision trees for corner point detection (such trees are used in computer vision for object tracking), study of systems of decision rules derived from decision trees, and comparison of different greedy algorithms for decision tree construction as single- and bi-criteria optimization algorithms.Citation
Chikalov I, Hussain S, Moshkov M (2017) Bi-Criteria Optimization of Decision Trees with Applications to Data Analysis. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.10.021.Sponsors
Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We are greatly indebted to the anonymous reviewers for useful comments and suggestions.Publisher
Elsevier BVAdditional Links
http://www.sciencedirect.com/science/article/pii/S0377221717309347ae974a485f413a2113503eed53cd6c53
10.1016/j.ejor.2017.10.021