Application of feature selection to data fusion based on improved perceptron and GAPSO

Author

Parizi Nejad, Z. ; Naeini, V.S.

Author_Institution

Dept. of Comput. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran

fYear

2014

fDate

27-29 May 2014

Firstpage

83

Lastpage

87

Abstract

In this article, Relief algorithm based on feature selection has been used for data fusion since it has a better time complexity compared to rough set theory. In the other section of the article, Genetic Algorithm Particle Swarm Optimization (GAPSO) and improved Perceptron methods are used to train the network. Experiments are performed in different iterations; meanwhile the improved Perceptron and GAPSO algorithms are compared together, based on Quality of Train (QoT) and Efficiency (Ef). Improved Perceptron algorithm develops a tradeoff between test data efficiency and QoT; however, GAPSO algorithm works comparably better than Perceptron in terms of data efficiency.

Keywords

feature selection; genetic algorithms; particle swarm optimisation; sensor fusion; GAPSO; QoT; data fusion; feature selection; genetic algorithm particle swarm optimization; improved perceptron algorithm; quality of train; GAPSO algorithm; Internet of Things; Perceptron algorithm; Relief algorithm; data fusion; feature selection; wireless sensor network;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Knowledge Technology (IKT), 2014 6th Conference on

Conference_Location

Shahrood

Print_ISBN

978-1-4799-5658-6

Type

conf

DOI

10.1109/IKT.2014.7030338

Filename

7030338