C4.5: programs for machine learning
C4.5: programs for machine learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
ACM Computing Surveys (CSUR)
A Tool to Obtain a Hierarchical Qualitative Rules form Quantitative Data
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
Data set Editing by Ordered Projection
Intelligent Data Analysis
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
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This paper describes an approach based on evolutionary algorithms, HIDER (HIerarchical DEcision Rules), 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 be therefore tried in order until one is found whose conditions are satisfied. Due to the computational cost of the evolutionary algorithms, we have developed a preprocesing method to reduce the number of examples from the database. This method, named EOP (Editing by Ordered Projections), has some interesting characteristics, especially from the point of view of the application of axis‐parallel classifiers. We have tested our system on real data from the UCI Repository, and the results of a 10‐fold cross‐validation are compared to C4.5's and C4.5Rules'. The experiments showed that HIDER works well in practice.