On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation

Majid Mahrooghy; Nicolas H. Younan; Valentine G. Anantharaj; James Aanstoos; Shantia Yarahmadian

doi:10.1109/LGRS.2012.2187513

On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation

Majid Mahrooghy, Nicolas H. Younan, Valentine G. Anantharaj, James Aanstoos, Shantia Yarahmadian

Data Lifecycle Technologies

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.

Original language	English
Article number	6172550
Pages (from-to)	963-967
Number of pages	5
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	9
Issue number	5
DOIs	https://doi.org/10.1109/LGRS.2012.2187513
State	Published - 2012

Funding

Manuscript received August 25, 2011; revised December 3, 2011; accepted January 2, 2012. Date of publication March 21, 2012; date of current version May 29, 2012. This work was supported by the National Aeronautics and Space Administration under Grant NNS06AA98B and the National Oceanic and Atmospheric Administration under Grant NA07OAR4170517.

Funders	Funder number
National Aeronautics and Space Administration	NNS06AA98B
National Oceanic and Atmospheric Administration	NA07OAR4170517

Keywords

Clustering
feature extraction
satellite precipitation estimation (SPE)
self-organizing map
unsupervised feature selection

Access to Document

10.1109/LGRS.2012.2187513

Cite this

@article{6212ae898b7f4194aeb6d9b96d003139,

title = "On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation",

abstract = "A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.",

keywords = "Clustering, feature extraction, satellite precipitation estimation (SPE), self-organizing map, unsupervised feature selection",

author = "Majid Mahrooghy and Younan, {Nicolas H.} and Anantharaj, {Valentine G.} and James Aanstoos and Shantia Yarahmadian",

year = "2012",

doi = "10.1109/LGRS.2012.2187513",

language = "English",

volume = "9",

pages = "963--967",

journal = "IEEE Geoscience and Remote Sensing Letters",

issn = "1545-598X",

publisher = "Institute of Electrical and Electronics Engineers",

number = "5",

}

TY - JOUR

T1 - On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation

AU - Mahrooghy, Majid

AU - Younan, Nicolas H.

AU - Anantharaj, Valentine G.

AU - Aanstoos, James

AU - Yarahmadian, Shantia

PY - 2012

Y1 - 2012

N2 - A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.

AB - A feature selection technique is used to enhance the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched by wavelet features. The feature selection technique includes a feature similarity selection method and a filter-based feature selection using genetic algorithm (FFSGA). It is employed in this study to find an optimal set of features where redundant and irrelevant features are removed. The entropy index fitness function is used to evaluate the feature subsets. The results show that using the feature selection technique not only improves the equitable threat score by almost 7% at some threshold values for the winter season, but also it extremely decreases the dimensionality. The bias also decreases in both the winter (January and February) and summer (June, July, and August) seasons.

KW - Clustering

KW - feature extraction

KW - satellite precipitation estimation (SPE)

KW - self-organizing map

KW - unsupervised feature selection

UR - http://www.scopus.com/inward/record.url?scp=84861927279&partnerID=8YFLogxK

U2 - 10.1109/LGRS.2012.2187513

DO - 10.1109/LGRS.2012.2187513

M3 - Article

AN - SCOPUS:84861927279

SN - 1545-598X

VL - 9

SP - 963

EP - 967

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

IS - 5

M1 - 6172550

ER -

On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this