Investigation of statistical learning theory performance on classification of multiple threshold values of metal content İstatistiksel öǧrenme teorisi ile metal içeriǧinin çoklu sinir deǧerlerinde siniflandirma performansinin incelenmesi


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ERTUNÇ G.

Scientific Mining Journal, vol.56, no.4, pp.166-172, 2017 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 56 Issue: 4
  • Publication Date: 2017
  • Doi Number: 10.30797/madencilik.391917
  • Journal Name: Scientific Mining Journal
  • Journal Indexes: Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.166-172
  • Keywords: Cadmium, Classification, Machine learning, Support vector machines
  • Hacettepe University Affiliated: Yes

Abstract

The necessity of classifying the data according to the categorical variable is quite common in earth sciences. Especially in mining, classification regarding to the metal content, which is covered in the study, classification of geological zones for mineral resource estimation or classification of blocks in the mining production phase can be given as an example of classification problems. Geostatistical estimations methods such as kriging cannot be regarded as solution for classification, and in this study it is clearly shown by comparative case study example. In the study, support vector machines algorithm is coded that classifies depending upon position of the data, based on the statistical learning theory, which can classify multiple and binary classes. The parameter selection is automatically integrated into the algorithm. By using the categorical variables depending on the continuous independent variables from collected data, algorithm reveals the categories in the unknown locations by using only the distance based information. Through introduced algorithm in the study, categorical variables related to independent variables can be classified with respected to the definition of the problem.