Application of uranium mineral band feature sub-set selection based on genetic algorithm

Yiping Tong; Zhihua Cai; Jia Wu

doi:10.1109/CICN.2013.137

Application of uranium mineral band feature sub-set selection based on genetic algorithm

Yiping Tong, Zhihua Cai, Jia Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search algorithm uses the four standards as fitness functions to select the best feature points. Then set three different sub-intervals, extend the best feature points to the best feature sub-sets. Finally, the best feature sub-sets are used for classification. Experiments show that information gain and symmetrical uncertainty that based on genetic algorithm are better than based on CFS in classification.

Original language	English
Title of host publication	2013 5th International Conference and Computational Intelligence and Communication Networks
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	626-630
Number of pages	5
ISBN (Electronic)	9780769550695
DOIs	https://doi.org/10.1109/CICN.2013.137
Publication status	Published - 1 Sep 2013
Externally published	Yes
Event	2013 5th International Conference and Computational Intelligence and Communication Networks - Mathura, India Duration: 27 Sep 2013 → 29 Sep 2013

Conference

Conference	2013 5th International Conference and Computational Intelligence and Communication Networks
Country/Territory	India
City	Mathura
Period	27/09/13 → 29/09/13

Keywords

entropy
genetic algorithms
search problems
uranium
uranium mineral band feature sub-set selection
genetic algorithm
absorption band position
feature-based metrics
information entropy
information gain
symmetrical uncertainty
type space
random search algorithm
fitness functions
CFS
Genetic algorithms
Minerals
Information entropy
Classification algorithms
Accuracy
Uncertainty
Absorption
feature sub-set
symmetric uncertainty
classification

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10.1109/CICN.2013.137

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title = "Application of uranium mineral band feature sub-set selection based on genetic algorithm",

abstract = "Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search algorithm uses the four standards as fitness functions to select the best feature points. Then set three different sub-intervals, extend the best feature points to the best feature sub-sets. Finally, the best feature sub-sets are used for classification. Experiments show that information gain and symmetrical uncertainty that based on genetic algorithm are better than based on CFS in classification.",

keywords = "entropy, genetic algorithms, search problems, uranium, uranium mineral band feature sub-set selection, genetic algorithm, absorption band position, feature-based metrics, information entropy, information gain, symmetrical uncertainty, type space, random search algorithm, fitness functions, CFS, Genetic algorithms, Minerals, Information entropy, Classification algorithms, Accuracy, Uncertainty, Absorption, feature sub-set, symmetric uncertainty, classification",

author = "Yiping Tong and Zhihua Cai and Jia Wu",

year = "2013",

month = sep,

day = "1",

doi = "10.1109/CICN.2013.137",

language = "English",

pages = "626--630",

booktitle = "2013 5th International Conference and Computational Intelligence and Communication Networks",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2013 5th International Conference and Computational Intelligence and Communication Networks ; Conference date: 27-09-2013 Through 29-09-2013",

}

Tong, Y, Cai, Z & Wu, J 2013, Application of uranium mineral band feature sub-set selection based on genetic algorithm. in 2013 5th International Conference and Computational Intelligence and Communication Networks. Institute of Electrical and Electronics Engineers (IEEE), pp. 626-630, 2013 5th International Conference and Computational Intelligence and Communication Networks, Mathura, India, 27/09/13. https://doi.org/10.1109/CICN.2013.137

Application of uranium mineral band feature sub-set selection based on genetic algorithm. / Tong, Yiping; Cai, Zhihua; Wu, Jia.

2013 5th International Conference and Computational Intelligence and Communication Networks. Institute of Electrical and Electronics Engineers (IEEE), 2013. p. 626-630.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T1 - Application of uranium mineral band feature sub-set selection based on genetic algorithm

AU - Tong, Yiping

AU - Cai, Zhihua

AU - Wu, Jia

PY - 2013/9/1

Y1 - 2013/9/1

N2 - Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search algorithm uses the four standards as fitness functions to select the best feature points. Then set three different sub-intervals, extend the best feature points to the best feature sub-sets. Finally, the best feature sub-sets are used for classification. Experiments show that information gain and symmetrical uncertainty that based on genetic algorithm are better than based on CFS in classification.

AB - Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search algorithm uses the four standards as fitness functions to select the best feature points. Then set three different sub-intervals, extend the best feature points to the best feature sub-sets. Finally, the best feature sub-sets are used for classification. Experiments show that information gain and symmetrical uncertainty that based on genetic algorithm are better than based on CFS in classification.

KW - entropy

KW - genetic algorithms

KW - search problems

KW - uranium

KW - uranium mineral band feature sub-set selection

KW - genetic algorithm

KW - absorption band position

KW - feature-based metrics

KW - information entropy

KW - information gain

KW - symmetrical uncertainty

KW - type space

KW - random search algorithm

KW - fitness functions

KW - CFS

KW - Genetic algorithms

KW - Minerals

KW - Information entropy

KW - Classification algorithms

KW - Accuracy

KW - Uncertainty

KW - Absorption

KW - feature sub-set

KW - symmetric uncertainty

KW - classification

U2 - 10.1109/CICN.2013.137

DO - 10.1109/CICN.2013.137

M3 - Conference proceeding contribution

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BT - 2013 5th International Conference and Computational Intelligence and Communication Networks

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2013 5th International Conference and Computational Intelligence and Communication Networks

Y2 - 27 September 2013 through 29 September 2013

ER -

Application of uranium mineral band feature sub-set selection based on genetic algorithm

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