Clonal Selection Algorithm for Feature Selection and Parameters Optimization of Support Vector Machines

This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of support vector machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:KAM 2009 : proceedings : 2009 second International Symposium on Knowledge Acquisition and Modeling : 30 November - 1 December 2009, Wuhan, China Ročník 2; s. 17 - 20
Hlavní autoři: Sheng Ding, ShunXin Li
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2009
Témata:
Artificial Immune System (AIS)
Artificial immune systems
Clonal Selection Algorithm
Feature selection
Genetic algorithms
Kernel
Knowledge acquisition
Optimization methods
Paper technology
Polynomials
Remote sensing
Support vector machine classification
Support Vector Machine(SVM)
Support vector machines
ISBN:9780769538884, 0769538886
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of support vector machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to improve classification accuracy, the clonal selection algorithm and genetic algorithm are used to reach the optimization performances with several real-world datasets. The experiments show the effectiveness of the methods. And those results are compared each other. The experiments denote that the proposed clonal selection algorithm is shown to be an evolutionary strategy capable of improving the classification accuracy and has fewer features for support vector machines.
ISBN:9780769538884
0769538886
DOI:10.1109/KAM.2009.86