Recent advances in error rate estimation
Pattern Recognition Letters
Bias of Nearest Neighbor Error Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Error Rate Estimation for k-NN Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bootstrap Technique for Nearest Neighbor Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On simultaneous selection of prototypes and features in large data
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
Application of bootstrap and other resampling techniques: Evaluation of classifier performance
Pattern Recognition Letters
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Mining conditional patterns in a database
Pattern Recognition Letters
Hybrid approaches for clustering
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Pattern synthesis using fuzzy partitions of the feature set for nearest neighbor classifier design
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
| Hi-index | 0.10 |
Nearest neighbor (NN) classifier is a popular non-parametric classifier. It is conceptually a simple classifier and shows good performance. Due to the curse of dimensionality effect, the size of training set needed by it to achieve a given classification accuracy becomes prohibitively large when the dimensionality of the data is high. Generating artificial patterns can reduce this effect. In this paper, we propose a novel pattern synthesis method called partition based pattern synthesis which can generate an artificial training set of exponential order when compared with that of the given original training set. We also propose suitable faster NN based methods to work with the synthetic training patterns. Theoretically, the relationship between our methods and conventional NN methods is established. The computational requirements of our methods are also theoretically established. Experimental results show that NN based classifiers with synthetic training set can outperform conventional NN classifiers and some other related classifiers.