Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Machine Learning
Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Local Attribute Value Grouping for Lazy Rule Induction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
On the Efficiency of Nearest Neighbor Searching with Data Clustered in Lower Dimensions
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Center-Based Indexing for Nearest Neighbors Search
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On k-NN Method with Preprocessing
Fundamenta Informaticae
Learning Sunspot Classification
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
A Rough Set Approach to Multiple Classifier Systems
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Solving Regression by Learning an Ensemble of Decision Rules
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Improved Classification for Problem Involving Overlapping Patterns
IEICE - Transactions on Information and Systems
On the appropriateness of evolutionary rule learning algorithms for malware detection
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
On combined classifiers, rule induction and rough sets
Transactions on rough sets VI
ENDER: a statistical framework for boosting decision rules
Data Mining and Knowledge Discovery
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
Combination of metric-based and rule-based classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
A hierarchical approach to multimodal classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
The rough set exploration system
Transactions on Rough Sets III
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
Multimodal classification: case studies
Transactions on Rough Sets V
Affect prediction from physiological measures via visual stimuli
International Journal of Human-Computer Studies
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The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to achieve classification accuracy comparable to an algorithm considering the whole learning set. The combination of k-NN and a rule-based algorithm results in a significant acceleration of the algorithm using all minimal rules. Moreover, the presented classifier has high accuracy for both kinds of domains: more suitable for k-NN classifiers and more suitable for rule based classifiers.