Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
Analysis of new techniques to obtain quality training sets
Pattern Recognition Letters - Special issue: Sibgrapi 2001
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Weighted Metrics to Minimize Nearest-Neighbor Classification Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Particle swarm optimization for prototype reduction
Neurocomputing
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
A novel template reduction approach for the K-nearest neighbor method
IEEE Transactions on Neural Networks
Applied Intelligence
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Nearest Neighbor Algorithm of Local Probability Centers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data stream classification with artificial endocrine system
Applied Intelligence
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The k nearest neighbor is a lazy learning algorithm that is inefficient in the classification phase because it needs to compare the query sample with all training samples. A template reduction method is recently proposed that uses only samples near the decision boundary for classification and removes those far from the decision boundary. However, when class distributions overlap, more border samples are retrained and it leads to inefficient performance in the classification phase. Because the number of reduced samples are limited, using an appropriate feature reduction method seems a logical choice to improve classification time. This paper proposes a new prototype reduction method for the k nearest neighbor algorithm, and it is based on template reduction and ViSOM. The potential property of ViSOM is displaying the topology of data on a two-dimensional feature map, it provides an intuitive way for users to observe and analyze data. An efficient classification framework is then presented, which combines the feature reduction method and the prototype selection algorithm. It needs a very small data size for classification while keeping recognition rate. In the experiments, both of synthetic and real datasets are used to evaluate the performance. Experimental results demonstrate that the proposed method obtains above 70聽% speedup ratio and 90聽% compression ratio while maintaining similar performance to kNN.