Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Information Processing Letters
Inferring decision trees using the minimum description length principle
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
RIEVL: Recursive Induction Learning in Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Intelligent Systems
A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Data-intensive analytics for predictive modeling
IBM Journal of Research and Development
Mathematical sciences in the nineties
IBM Journal of Research and Development
The minimum consistent subset cover problem and its applications in data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Switching neural networks: a new connectionist model for classification
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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Generating classification rules or decision trees from examples has been a subject of intense study in the pattern recognition community, the statistics community, and the machine-learning community of the artificial intelligence area. We pursue a point of view that minimality of rules is important, perhaps above all other considerations (biases) that come into play in generating rules. We present a new minimal rule-generation algorithm called R-MINI (Rule-MINI) that is an adaptation of a well-established heuristic-switching-function-minimization technique, MINI. The main mechanism that reduces the number of rules is repeated application of generalization and specialization operations to the rule set while maintaining completeness and consistency. R-MINI results on some benchmark cases are also presented.