Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Initializing neural networks using decision trees
Computational learning theory and natural learning systems: Volume IV
The Limitations of Decision Trees and Automatic Learning in Real World Medical Decision Making
CBMS'97 Proceedings of the 10th conference on Computer based medical systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
Evolutionary approaches to fuzzy modelling for classification
The Knowledge Engineering Review
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Decision trees have been already successfully used in medicine, but as in traditional statistics, some hard real world problems cannot be solved successfully using the traditional way of induction. In our experiments, we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the orthopaedic fracture data with 2637 cases, described by 23 attributes and a decision with 3 possible values, we built decision trees with four classical approaches, one hybrid approach where we combined neural networks and decision trees, and with evolutionary approach. The results show, that all approaches had problems with accuracy, sensitivity, or decision tree size. The comparison shows that the best compromise in hard real world problem decision trees building is the evolutionary approach.