Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Voting over Multiple Condensed Nearest Neighbors
Artificial Intelligence Review - Special issue on lazy learning
Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Genetic algorithms for generation of class boundaries
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Knowledge and Data Engineering
Cluster-based nearest-neighbour classifier and its application on the lightning classification
Journal of Computer Science and Technology
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
On optimum choice of k in nearest neighbor classification
Computational Statistics & Data Analysis
An efficient nearest neighbor classifier using an adaptive distance measure
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Bayesian multiscale smoothing in supervised and semi-supervised kernel discriminant analysis
Computational Statistics & Data Analysis
On hybrid classification using model assisted posterior estimates
Pattern Recognition
Efficient model selection for large-scale nearest-neighbor data mining
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
A probabilistic approach for semi-supervised nearest neighbor classification
Pattern Recognition Letters
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Nearest neighbor classification is one of the simplest and most popular methods for statistical pattern recognition. A major issue in k-nearest neighbor classification is how to find an optimal value of the neighborhood parameter k. In practice, this value is generally estimated by the method of cross-validation. However, the ideal value of k in a classification problem not only depends on the entire data set, but also on the specific observation to be classified. Instead of using any single value of k, this paper studies results for a finite sequence of classifiers indexed by k. Along with the usual posterior probability estimates, a new measure, called the Bayesian measure of strength, is proposed and investigated in this paper as a measure of evidence for different classes. The results of these classifiers and their corresponding estimated misclassification probabilities are visually displayed using shaded strips. These plots provide an effective visualization of the evidence in favor of different classes when a given data point is to be classified. We also propose a simple weighted averaging technique that aggregates the results of different nearest neighbor classifiers to arrive at the final decision. Based on the analysis of several benchmark data sets, the proposed method is found to be better than using a single value of k.