Algorithm with Evenly Distributed Gaussian Function for Diatom Classification
Date Issued
2010
Author(s)
Abstract
The diatom organisms are good bio-indicators of certain
ecosystem environments. According the national directive for
water quality classification, each WQC represent a water
quantity of certain physico-chemical parameters in certain
range define by biological experts. The property of bioindicator is used to characterize the environment and thus
helping in process of classification of the diatoms in the
correct water quality classes (WQCs). In this direction we use
pattern trees; trees which have combined the advantages of
the information theory and fuzzy theory to model (predict) in
which WQC belongs the certain diatom. Because many of the
newly discover diatoms does not have ecological preference,
this algorithm significantly improves the process of fast and
accuracy classification. In our approach we divide each
diatom into three evenly ranges with Gaussian functions,
which will be represented with fuzzy terms (low, medium and
high) similar as the WQC range classes. Using this data
mining techniques we can closely reflect the very nature of
the diatoms dataset, which later the experiments will confirms
this assumption, by taking into account the mean and the
standard deviation of each diatom range. The experimental
results have shown that the extract knowledge has high level
of confidence factor in many cases and the trees obtained
have high accuracy compared with other classification
algorithms. As future work we intend to expand the number
of fuzzy membership and inspect their influence, to
implement more fuzzy aggregation functions and similarity
definitions in process of pattern trees.
ecosystem environments. According the national directive for
water quality classification, each WQC represent a water
quantity of certain physico-chemical parameters in certain
range define by biological experts. The property of bioindicator is used to characterize the environment and thus
helping in process of classification of the diatoms in the
correct water quality classes (WQCs). In this direction we use
pattern trees; trees which have combined the advantages of
the information theory and fuzzy theory to model (predict) in
which WQC belongs the certain diatom. Because many of the
newly discover diatoms does not have ecological preference,
this algorithm significantly improves the process of fast and
accuracy classification. In our approach we divide each
diatom into three evenly ranges with Gaussian functions,
which will be represented with fuzzy terms (low, medium and
high) similar as the WQC range classes. Using this data
mining techniques we can closely reflect the very nature of
the diatoms dataset, which later the experiments will confirms
this assumption, by taking into account the mean and the
standard deviation of each diatom range. The experimental
results have shown that the extract knowledge has high level
of confidence factor in many cases and the trees obtained
have high accuracy compared with other classification
algorithms. As future work we intend to expand the number
of fuzzy membership and inspect their influence, to
implement more fuzzy aggregation functions and similarity
definitions in process of pattern trees.
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