Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Inductive learning supported by integer programming
Computers and Artificial Intelligence
Code recognition and set selection with neural networks
Code recognition and set selection with neural networks
Fuzzy logic with linguistic quantifiers in inductive learning
Fuzzy logic for the management of uncertainty
Minimal cost set covering using probabilistic methods
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Industrial Applications of Neural Networks: Project Annie Handbook
Industrial Applications of Neural Networks: Project Annie Handbook
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
An Improved Inductive Learning Algorithm with a Preanalysis of Data
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A softened formulation of inductive learning and its use for coronary disease data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
A knowledge-based model of parliamentary election
Information Sciences: an International Journal
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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In this paper we propose an inductive learning method, IP2, 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. A pre-analysis of data is included that assigns higher weights to those values of attributes which occur more often in the positive than in the negative examples. The inductive learning problem is represented as a modification of the set covering problem which are solved by an integer programming based algorithm using elements of a greedy algorithm or a genetic algorithm. The results are very encouraging and are illustrated on thyroid cancer and coronary heard disease problems.