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SDL, a stochastic algorithm for learning decision lists with limited complexity

SDL, a stochastic algorithm for learning decision lists with limited complexity This paper deals with learning decision lists from examples. In real world problems, data are often noisy and imperfectly described. It is commonly acknowledged that in such cases, consistent but inevitably complex classification procedures usually cause overfitting: results are perfect on the learning set but worse on new examples. Therefore, one searches for less complex procedures which are almost consistent or, in other words, for a good compromise between complexity and goodness-of-fit. But such a requirement generally involves NP-completeness. In a way, CN2 provides a greedy approach to the problem. In this paper, we propose to search the solution space more extensively, using a stochastic procedure, an association of simulated annealing (SA) and simple tabu search (TS) in two distinct phases. In the first phase, we use SA to diversify the search. In the second phase, TS intensifies the search. We compare CART, CN2, and our method using natural and artificial domains. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

SDL, a stochastic algorithm for learning decision lists with limited complexity

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References (32)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/BF01530954
Publisher site
See Article on Publisher Site

Abstract

This paper deals with learning decision lists from examples. In real world problems, data are often noisy and imperfectly described. It is commonly acknowledged that in such cases, consistent but inevitably complex classification procedures usually cause overfitting: results are perfect on the learning set but worse on new examples. Therefore, one searches for less complex procedures which are almost consistent or, in other words, for a good compromise between complexity and goodness-of-fit. But such a requirement generally involves NP-completeness. In a way, CN2 provides a greedy approach to the problem. In this paper, we propose to search the solution space more extensively, using a stochastic procedure, an association of simulated annealing (SA) and simple tabu search (TS) in two distinct phases. In the first phase, we use SA to diversify the search. In the second phase, TS intensifies the search. We compare CART, CN2, and our method using natural and artificial domains.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Apr 5, 2005

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