Instance-Based Learning Algorithms
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Improving accuracy by combining rule-based and case-based reasoning
Artificial Intelligence
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
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
Covering with Reducts - A Fast Algorithm for Rule Generation
RSCTC '98 Proceedings of the First 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
Center-based indexing in vector and metric spaces
Fundamenta Informaticae
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
A Multi-Strategy Approach to KNN and LARM on Small and Incrementally Induced Prediction Knowledge
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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We consider two classification approaches. The metric-based approach induces the distance measure between objects and classifies new objects on the basis of their nearest neighbors in the training set. The rule-based approach extracts rules from the training set and uses them to classify new objects. In the paper we present a model that combines both approaches. In the combined model the notions of rule, rule minimality and rule consistency are generalized to metric-dependent form. An effective polynomial algorithm implementing the classification model based on minimal consistent rules has been proposed in [2]. We show that this algorithm preserves its properties in application to the metric-based rules. This allows us to combine this rule-based algorithm with the k nearest neighbor (k-nn) classification method. In the combined approach the rule-based algorithm takes the role of nearest neighbor voting model. The presented experiments with real data sets show that the combined classification model have the accuracy higher than single models.