Very Simple Classification Rules Perform Well on Most Commonly Used Datasets
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Summary
On most datasets studied, the best of very simple rules that classify examples on the basis of a single attribute is as accurate as the rules induced by the majority of machine learning systems.
- Type
- article
- Published
- 1993-04-01
- Cited by
- 1,851
- References
- 69
- Access
- Open access
- OpenAlex
- https://openalex.org/W2132166479
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6596
Keywords
Computer science, Artificial intelligence, Machine learning, Simple (philosophy), Basis (linear algebra)
References
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- Inductive knowledge acquisition: a case study
- The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains
- Structured induction in expert systems
- Induction with randomization testing: decision-oriented analysis of large data sets
- An Empirical Comparison of Pruning Methods for Decision Tree Induction
- Computer Intensive Methods in Statistics
- Induction in Noisy Domains
- Overfitting avoidance as bias
- Enhancing medical expert systems with knowledge obtained from statistical data
- Maximizing the Predictive Value of Production Rules
- Learning hard concepts through constructive induction: framework and rationale
- Knowledge Acquisition Via Incremental Conceptual Clustering
- Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System
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