Instance-Based Learning Algorithms
Machine Learning
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Using Lattice-Based Framework as a Tool for Feature Extraction
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Structural Machine Learning with Galois Lattice and Graphs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Exemplar-Based Prototype Selection for a Multi-Strategy Learning System
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
IGLUE: An Instance-based Learning System over Lattice Theory
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Exemplar-Based Prototype Selection for a Multi-Strategy Learning System
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
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Multi-strategy Learning (MSL) consists of combining at least two different learning strategies to bring out a powerful system, where the drawbacks of the basic algorithms are avoided. In this scope, instance-based learning (IBL) techniques are often used as the basic component. However one of the major drawback of IBL is the prototype selection problem which consists in selecting a subset of representative instances in order to reduce the classification process. This paper presents a novel approach which consists of three steps. The first one builds a set of lattice-based hypotheses that characterize the training data set. Given an unseen example, the second step selects a subset of training instances through the way they verify the same hypotheses as the unseen example. And finally the last step uses this subset of training instances as the prototypes for the classification of the unseen example. Results of experiments that we conducted show the effectiveness of our approach compared to standard ML techniques on different datasets.