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 proceedingConference proceeding contributionpeer-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 languageEnglish
Title of host publication2013 5th International Conference and Computational Intelligence and Communication Networks
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages626-630
Number of pages5
ISBN (Electronic)9780769550695
DOIs
Publication statusPublished - 1 Sep 2013
Externally publishedYes
Event2013 5th International Conference and Computational Intelligence and Communication Networks - Mathura, India
Duration: 27 Sep 201329 Sep 2013

Conference

Conference2013 5th International Conference and Computational Intelligence and Communication Networks
CountryIndia
CityMathura
Period27/09/1329/09/13

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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