An Efficient Ant Colony Instance Selection Algorithm for KNN Classification

Amal Miloud-Aouidate and Ahmed Riadh Baba-Ali
Additional contact information
Amal Miloud-Aouidate: University of Science and Technology, Houari Boumediene, Bab Ezzouar Algiers, Algeria
Ahmed Riadh Baba-Ali: University of Science and Technology, Houari Boumediene, Bab Ezzouar Algiers, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2013, vol. 4, issue 3, 47-64

Abstract: The extraordinary progress in the computer sciences field has made Nearest Neighbor techniques, once considered impractical from a standpoint of computation (Dasarathy et al., 2003), became feasible for real-world applications. In order to build an efficient nearest neighbor classifier two principal objectives have to be reached: 1) achieve a high accuracy rate; and 2) minimize the set of instances to make the classifier scalable even with large datasets. These objectives are not independent. This work addresses the issue of minimizing the computational resource requirements of the KNN technique, while preserving high classification accuracy. This paper investigates a new Instance Selection method based on Ant Colonies Optimization principles, called Ant Instance Selection (Ant-IS) algorithm. The authors have proposed in a previous work (Miloud-Aouidate & Baba-Ali, 2012a) to use Ant Colony Optimization for preprocessing data for Instance Selection. However to the best of the authors’ knowledge, Ant Metaheuristic has not been used in the past for directly addressing Instance Selection problem. The results of the conducted experiments on several well known data sets are presented and compared to those obtained using a number of well known algorithms, and most known classification techniques. The results provide evidence that: (1) Ant-IS is competitive with the well-known kNN algorithms; (2) The condensed sets computed by Ant-IS offers also better classification accuracy then those obtained by the compared algorithms.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijamc.2013070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:4:y:2013:i:3:p:47-64

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().