AI Article Synopsis
- The paper presents HIDER, an evolutionary algorithm that generates hierarchical decision rules for learning in both continuous and discrete domains.
- The resulting rules are organized in a way that allows for sequential testing, potentially reducing the total number of rules by nesting them.
- Performance evaluations using data from the UCI repository show that HIDER outperforms established methods like C4.5 and See5 in practical applications.
Article Abstract
This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.
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| http://dx.doi.org/10.1109/TSMCB.2002.805696 | DOI Listing |
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