Skip to Main content Skip to Navigation
Conference papers

Feature Selection for Classification Using an Ant System Approach

Abstract : Many applications such as pattern recognition and data mining require selecting a subset of the input features in order to represent the whole set of features. The aim of feature selection is to remove irrelevant, redundant or noisy features while keeping the most informative ones. In this paper, an ant system approach for solving feature selection for classification is presented. The results we got are promising in terms of the accuracy of the classifier and the number of selected features in all the used datasets.
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-01054496
Contributor : Hal Ifip <>
Submitted on : Thursday, August 7, 2014 - 8:49:55 AM
Last modification on : Thursday, March 5, 2020 - 5:40:48 PM
Long-term archiving on: : Wednesday, November 26, 2014 - 1:25:27 AM

File

final_02.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Nadia Abd-Alsabour. Feature Selection for Classification Using an Ant System Approach. 7th IFIP TC 10 Working Conference on Distributed, Parallel and Biologically Inspired Systems (DIPES) / 3rd IFIP TC 10 International Conference on Biologically-Inspired Collaborative Computing (BICC) / Held as Part of World Computer Congress (WCC) , Sep 2010, Brisbane, Australia. pp.233-241, ⟨10.1007/978-3-642-15234-4_23⟩. ⟨hal-01054496⟩

Share

Metrics

Record views

320

Files downloads

803