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
Instance-Based Classification by Emerging Patterns
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Discovery of Decision Rules by Matching New Objects Against Data Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Local Attribute Value Grouping for Lazy Rule Induction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Combining the Strength of Pattern Frequency and Distance for Classification
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
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
Improving rule-based systems through case-based reasoning
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Center-based indexing in vector and metric spaces
Fundamenta Informaticae
Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
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
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Center-Based Indexing in Vector and Metric Spaces
Fundamenta Informaticae
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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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.