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ICML
2004
IEEE

Lookahead-based algorithms for anytime induction of decision trees

11 years 5 months ago
Lookahead-based algorithms for anytime induction of decision trees
The majority of the existing algorithms for learning decision trees are greedy--a tree is induced top-down, making locally optimal decisions at each node. In most cases, however, the constructed tree is not globally optimal. Furthermore, the greedy algorithms require a fixed amount of time and are not able to generate a better tree if additional time is available. To overcome this problem, we present two lookahead-based algorithms for anytime induction of decision trees, thus allowing tradeoff between tree quality and learning time. The first one is depth-k lookahead, where a larger time allocation permits larger k. The second algorithm uses a novel strategy for evaluating candidate splits; a stochastic version of ID3 is repeatedly invoked to estimate the size of the tree in which each split results, and the one that minimizes the expected size is preferred. Experimental results indicate that for several hard concepts, our proposed approach exhibits good anytime behavior and yields si...
Saher Esmeir, Shaul Markovitch
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Saher Esmeir, Shaul Markovitch
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