Incremental class learning approach and its application to handwritten digit recognition
Information Sciences—Informatics and Computer Science: An International Journal
Boosting the distance estimation
Pattern Recognition Letters
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of Instance Typicality for Efficient Detection of Outliers with Neural Network Classifiers
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
Including metric space topology in neural networks training by ordering patterns
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Probability-Based Distance Function for Distance-Based Classifiers
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Classification Based on Combination of Kernel Density Estimators
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
Statistically---Induced kernel function for support vector machine classifier
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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In the paper a method of training set selection, in case of low data availability, is proposed and experimentally evaluated with the use of k-NNand neural classifiers. Application of proposed approach visibly improves the results compared to the case of training without postulated enhancements.Moreover, a new measure of distance between events in the pattern space is proposed and tested with k-NNmodel. Numerical results are very promising and outperform the reference literature results of k-NNclassifiers built with other distance measures.