Fuzziness vs. probability in a data mining application for soil classification

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data mining methods have been proven effective in extracting knowledge from existing data sources for the classification of soils. Previous studies have suggested that soils are spatial entities with fuzzy boundaries and prompted the development of data mining methods to extract knowledge that allows for fuzzy classifications of soils. This paper first looks at the nature of soil classification from the perspective of cognitive psychology. It then examines data mining methods used for fuzzy soil classification. It notes that some of the methods are inherently hybrids that combine statistical measures with fuzzy models on sound cognitive bases. This paper reflects upon the long lasting debate on fuzziness versus probability for modeling uncertainties and suggests that hybrid models are valid both practically and cognitively. At last, some preliminary results are reported in comparing pure probabilistic methods (Bayesian), a fuzzy method, and two hybrid approaches to knowledge discovery for soil classification that supports the suggestion.

Original languageEnglish
Title of host publicationProceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Pages2614-2618
Number of pages5
DOIs
StatePublished - 2010
Event2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010 - Yantai, Shandong, China
Duration: 10 Aug 201012 Aug 2010

Publication series

NameProceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Volume6

Conference

Conference2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Country/TerritoryChina
CityYantai, Shandong
Period10/08/1012/08/10

Keywords

  • Cognitive
  • Data mining
  • Fuzzy logic
  • Probability
  • Soil classification

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