6533b7d7fe1ef96bd1268d79
RESEARCH PRODUCT
Statistical classification and proportion estimation - an application to a macroinvertebrate image database
Tuomas TurpeinenSalme KärkkäinenJohanna ÄRjeKristian Meissnersubject
Computer sciencebusiness.industryFeature extractionDecision treeConfusion matrixPattern recognitionBayes classifierDistance measuresStatistical classificationBayes' theoremStatisticsBayes error rateArtificial intelligencebusinessdescription
We apply and compare a random Bayes forest classifier and three traditional classification methods to a dataset of complex benthic macroinvertebrate images of known taxonomical identity. Since in biomonitoring changes in benthic macroinvertebrate taxa proportions correspond to changes in water quality, their correct estimation is pivotal. As classification errors are passed on to the allocated proportions, we explore a correction method known as a confusion matrix correction. Classification methods were compared using the misclassification error and the χ2 distance measures of the true proportions to the allocated and to the corrected proportions. Using low misclassification error and smallest χ2 distance measures as performance criteria the classical Bayes classifier performed best followed closely by the random Bayes forest.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-08-01 | 2010 IEEE International Workshop on Machine Learning for Signal Processing |