A stochastic approach to wilson's editing algorithm

Authors:
Fernando Vázquez;J. Salvador Sánchez;Filiberto Pla
Affiliations:
Dept de Ciencia de la Computación, Universidad de Oriente, Santiago de Cuba, Cuba;Dept. Lenguajes y Sistemas Informáticos, Universitat Jaume I, Castellón, Spain;Dept. Lenguajes y Sistemas Informáticos, Universitat Jaume I, Castellón, Spain
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

Citing 3
Cited 10

Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Analysis of new techniques to obtain quality training sets

Pattern Recognition Letters - Special issue: Sibgrapi 2001
Considerations about sample-size sensitivity of a family of editednearest-neighbor rules

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

An offline/real-time artifact rejection strategy to improve the classification of multi-channel evoked potentials

Pattern Recognition
Learning and Forgetting with Local Information of New Objects

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A review of instance selection methods

Artificial Intelligence Review
A new editing scheme based on a fast two-string median computation applied to OCR

SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Prototype reduction techniques: A comparison among different approaches

Expert Systems with Applications: An International Journal
On the use of data filtering techniques for credit risk prediction with instance-based models

Expert Systems with Applications: An International Journal
Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification

Pattern Recognition
An improved fast edit approach for two-string approximated mean computation applied to OCR

Pattern Recognition Letters
Semi-supervised multi-label image classification based on nearest neighbor editing

Neurocomputing
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two extensions of the original Wilson's editing method are introduced in this paper. These new algorithms are based on estimating probabilities from the k-nearest neighbor patterns of an instance, in order to obtain more compact edited sets while maintaining the classification rate. Several experiments with synthetic and real data sets are carried out to illustrate the behavior of the algorithms proposed here and compare their performance with that of other traditional techniques.