Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An integer programming approach to inductive learning using genetic and greedy algorithms
New learning paradigms in soft computing
An Improved Inductive Learning Algorithm with a Preanalysis of Data
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Computing with Words in Information/Intelligent Systems 2: Applications
Computing with Words in Information/Intelligent Systems 2: Applications
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
“Rule + exception” strategies for knowledge management and discovery
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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We present an improved inductive learning method to derive classification rules that correctly describe most of the examples belonging to a class and do not describe most of the examples not belonging to this class. The problem is represented as a modification of the set covering problems solved by a genetic algorithm. Its is employed to medical data on coronary disease, and the results seem to be encouraging.