Name
Title | ||
|---|---|---|
Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter. |
Abstract | ||
|---|---|---|
This paper presents a hybrid classification algorithm, ACOMV-SVM which is based on ant colony and support vector machine. A new direction for ant colony optimisation is to optimise mixed discrete and continuous variables. The optimised variables are then feed into selecting feature subset and tuning its parameters are two main problems of SVM. Most approaches related to tuning support vector machine parameters will discretise the continuous value of the parameters which will give a negative effect on the performance. The objective of this paper is to formulate an algorithm for tuning SVM parameters and feature subset selection. This can be achieved by simultaneously performing the selection of feature subset and tuning SVM parameters tasks. ACO |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1504/IJBIC.2017.081842 | IJBIC |
Keywords | Field | DocType |
mixed-variable ACO, ACO(MV), support vector machine, SVM, tuning SVM parameters, feature subset selection, bio-inspired computation | Mathematical optimization,Feature selection,Pattern recognition,Swarm intelligence,Support vector machine,Algorithm,Continuous variable,Artificial intelligence,Ant colony,Mathematics,Machine learning,Metaheuristic | Journal |
Volume | Issue | ISSN |
9 | 1 | 1758-0366 |
Citations | PageRank | References |
0 | 0.34 | 39 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiba Basim Alwan | 1 | 0 | 0.34 |
| Ku Ruhana Ku Mahamud | 2 | 22 | 9.33 |