A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On optimization of decision trees
Transactions on Rough Sets IV
Relationships between depth and number of misclassifications for decision trees
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Comparison of greedy algorithms for α-decision tree construction
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Dynamic Programming Approach for Partial Decision Rule Optimization
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Dynamic programming approach to optimization of approximate decision rules
Information Sciences: an International Journal
Sequential optimization of binary search trees for multiple cost functions
CATS '11 Proceedings of the Seventeenth Computing: The Australasian Theory Symposium - Volume 119
Sequential optimization of binary search trees for multiple cost functions
CATS 2011 Proceedings of the Seventeenth Computing on The Australasian Theory Symposium - Volume 119
Relationships between Average Depth and Number of Misclassifications for Decision Trees
Fundamenta Informaticae - Dedicated to the Memory of Professor Manfred Kudlek
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The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1].