Ant Colony Prototype Reduction Algorithm for kNN Classification

Author

Miloud-Aouidate, A. ; Baba-Ali, A.R.

Author_Institution

Lab. of Robot., Univ. of Sci. & Technol. Houari Boumediene, Algiers, Algeria

fYear

2012

fDate

5-7 Dec. 2012

Firstpage

289

Lastpage

294

Abstract

Ant colony optimization (ACO) techniques were proposed to solve many combinatorial problems. As prototype selection (PS) is a combinatorial problem we attempt, in this paper, to address it using an Ant Colony algorithm. This work proposes an Ant Prototype Reducing algorithm (Ant-PR). The goal of this algorithm is to reduce the training set of the 1NN classifier. We compared the Ant-PR performances to two classical well-known kNN condensing algorithms. The results provide evidence that: (1) Ant-PR is competitive with the well-known kNN algorithms, (2) The condensed sets computed by Ant-PR offers better classification accuracy then those obtained by the compared algorithms.

Keywords

ant colony optimisation; combinatorial mathematics; data reduction; pattern classification; pattern clustering; 1NN classifier; ACO techniques; ant colony prototype reduction algorithm; ant-PR performances; combinatorial problem; condensed sets; k-nearest neighbor classification rule; kNN classification; prototype selection; training set; Accuracy; Algorithm design and analysis; Benchmark testing; Classification algorithms; Genetic algorithms; Prototypes; Training; Ant Colonies Optimisation; Condensing; KNN; Prototype Reduction; Prototype Selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Science and Engineering (CSE), 2012 IEEE 15th International Conference on

Conference_Location

Nicosia

Print_ISBN

978-1-4673-5165-2

Electronic_ISBN

978-0-7695-4914-9

Type

conf

DOI

10.1109/ICCSE.2012.47

Filename

6417306