Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
A hybrid nearest-neighbor and nearest-hyperrectangle algorithm
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The neural network model RuleNet and its application to mobile robot navigation
Fuzzy Sets and Systems - Special issue on methods for data analysis in classificatin and control
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Choosing the Initial Set of Exemplars when Learning with an NGE-based System
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
| Hi-index | 0.00 |
The Nested Generalized Exemplar (NGE) model is an incremental form of inductive learning that generalizes a given training set into hypotheses represented as a set of hyperrectangles in an n-dimensional Euclidean space. The NGE algorithm can be considered a descendent of either Nearest Neighbor (NN) or K-Nearest Neighbor (KNN) algorithms. NGE based systems classify new instances by calculating their similarity to the nearest generalized exemplar (i.e. hyperrectangle). Similarity in an NGE model is implemented by a distance metric namely the Euclidean distance. This paper describes a version of the NGE model suitable for fuzzy domains called Fuzzy NGE (F-NGE). F-NGE learns fuzzy rules for classifying instances into crisp classes. An implementation of F-NGE has been tested in several different knowledge domains for which results are presented and discussed. Results of fuzzy versions of NN and KNN using the same domains are also presented, for comparison.