Information Sciences: an International Journal
An adaptive hybrid and cluster-based model for speeding up the k-NN classifier
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A simple noise-tolerant abstraction algorithm for fast k-NN classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Efficient dataset size reduction by finding homogeneous clusters
Proceedings of the Fifth Balkan Conference in Informatics
AIB2: an abstraction data reduction technique based on IB2
Proceedings of the 6th Balkan Conference in Informatics
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
Prototype reduction based on Direct Weighted Pruning
Pattern Recognition Letters
Low-voltage CMOS current-mode exponential circuit with 70dB output dynamic range
Microelectronics Journal
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
On the use of meta-learning for instance selection: An architecture and an experimental study
Information Sciences: an International Journal
Hi-index | 0.00 |
The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve classification and pattern recognition tasks. Despite its high classification accuracy, this rule suffers from several shortcomings in time response, noise sensitivity, and high storage requirements. These weaknesses have been tackled by many different approaches, including a good and well-known solution that we can find in the literature, which consists of the reduction of the data used for the classification rule (training data). Prototype reduction techniques can be divided into two different approaches, which are known as prototype selection and prototype generation (PG) or abstraction. The former process consists of choosing a subset of the original training data, whereas PG builds new artificial prototypes to increase the accuracy of the NN classification. In this paper, we provide a survey of PG methods specifically designed for the NN rule. From a theoretical point of view, we propose a taxonomy based on the main characteristics presented in them. Furthermore, from an empirical point of view, we conduct a wide experimental study that involves small and large datasets to measure their performance in terms of accuracy and reduction capabilities. The results are contrasted through nonparametrical statistical tests. Several remarks are made to understand which PG models are appropriate for application to different datasets.