A hybrid KNN-ant colony optimization algorithm for prototype selection

Authors:
Amal Miloud-Aouidate;Ahmed Riadh Baba-Ali
Affiliations:
Laboratory of Robotics, Parallelism and Embedded, University of Sciences and Technology Houari Boumediene, USTHB, Algiers, Algeria;Laboratory of Robotics, Parallelism and Embedded, University of Sciences and Technology Houari Boumediene, USTHB, Algiers, Algeria
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Year:
2012

Citing 7
Cited 0

Reduction Techniques for Instance-BasedLearning Algorithms

Machine Learning
Fast condensed nearest neighbor rule

ICML '05 Proceedings of the 22nd international conference on Machine learning
A novel template reduction approach for the K-nearest neighbor method

IEEE Transactions on Neural Networks
A class boundary preserving algorithm for data condensation

Pattern Recognition
Nearest neighbor pattern classification

IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)

IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

The condensing KNN is the application of the K-Nearest Neighbors classifier with a condensed training set, which is a consistent subset calculated from the initial training set. In this work we present a novel algorithm, Ant-KNN, which allows improving the performance of the standard KNN classifier by a method based on ant colonies optimization. The results obtained through tests conducted on five benchmarks from UCI Machine Learning Repository demonstrate the improvement obtained by our algorithm in comparison with other condensing KNN algorithms.