Article: Feature selection intelligent algorithm with mutual information and steepest ascent strategy Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2013 Vol.5 No.4 pp.257 - 269 Abstract: Remote sensing is a higher technology to produce knowledge for data mining applications. In principle, hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe redundant information, or they are affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many studies use mutual information (MI) or normalised forms of MI to select appropriate bands. In this paper we design an algorithm based also on MI, and we combine MI with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial intelligent system to control redundancy; we had to clear the difference of the results algorithm and the human decision, and this can be viewed as case study which human decision is perhaps different to an intelligent algorithm. Inderscience Publishers - linking academia, business and industry through research

Title: Feature selection intelligent algorithm with mutual information and steepest ascent strategy

Authors: Elkebir Sarhrouni; Ahmed Hammouch; Driss Aboutajdine

Addresses: LRIT, Faculty of Sciences, Mohamed V – Agdal University, Morocco ' LRGE, ENSET, Mohamed V – Souissi University, Morocco ' LRIT, Faculty of Sciences, Mohamed V – Agdal University, Morocco

Abstract: Remote sensing is a higher technology to produce knowledge for data mining applications. In principle, hyperspectral images (HSIs) is a remote sensing tool that provides precise classification of regions. The HSI contains more than a hundred of images of the ground truth (GT) map. Some images are carrying relevant information, but others describe redundant information, or they are affected by atmospheric noise. The aim is to reduce dimensionality of HSI. Many studies use mutual information (MI) or normalised forms of MI to select appropriate bands. In this paper we design an algorithm based also on MI, and we combine MI with steepest ascent algorithm, to improve a symmetric uncertainty coefficient-based strategy to select relevant bands for classification of HSI. This algorithm is a feature selection tool and a wrapper strategy. We perform our study on HSI AVIRIS 92AV3C. This is an artificial intelligent system to control redundancy; we had to clear the difference of the results algorithm and the human decision, and this can be viewed as case study which human decision is perhaps different to an intelligent algorithm.

Keywords: hyperspectral images; HSIs; feature selection; mutual information; redundancy; steepest ascent; artificial intelligence; intelligence paradigms; intelligent selection; remote sensing; data mining; region classification; redundant information.

DOI: 10.1504/IJAIP.2013.058300

International Journal of Advanced Intelligence Paradigms, 2013 Vol.5 No.4, pp.257 - 269

Received: 14 Jan 2013
Accepted: 23 Mar 2013
Published online: 30 Jul 2014 *

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Title: Feature selection intelligent algorithm with mutual information and steepest ascent strategy

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