1. Machine Learning Applications in Biological Classification of River Water Quality Saso Dzeroski, Jasna Grobovic and William J. Walley 98419-548 조 동 연

2. Contents Introduction Learning Rules for Biological Classification of British Rivers  The Data  The Experiment Analysis of Data about Slovenian Rivers  The Influence of Physical and Chemical Parameters on Selected Organisms  Biological Classification Discussion

3. Introduction Indicator Organisms (Bioindicators)  Given a biological sample, information on the presence and density of all indicator organisms present in the sample is usually combined to derive a biological index that reflects the quality of the water as the site where the sample was taken Saprobic Index  The main Problem: subjectivity The subjectivity introduced at intermediate levels can and should be minimized.

4. Learning Rules for Biological Classification of British River Data  292 samples of 80 benthic macroinvertebrates  Abundance of animals  0: no members of the particular family  1: 1-2  2: 3-9  3: 10-49  4: 50-99  5: 100-999  6: more than 1000  Sparse matrix  Five classes

5. Experiments 1  Modified CN2 algorithm  Measure the relative information score  Use the m-estimate instead of the Laplace estimate  The rules were required to be highly significant (99%).  15 difference values of m were tried (0, 0.01, 0.25., 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256, 512 and 1024).  Criterion  Information Score  Accuracy  Smaller value of the parameter m

6. Result 1  12 rules, m = 32  83% accuracy on the training set, 75% information content  Each rule covered 25 examples and contained 5 conditions.  The expert’s conclusions confirmed the rules.

7. Experiment 2  The main criticism was that the rules use only a small number of taxa, whereas the expert takes into account the whole community.  Six additional attributes  MoreThan0, MoreThan1, …, MoreThan5  reflect the number of families Result 2  13 rules, m = 64  accuracy 84%, information content 80%

8. Experiment 3  195 training example, 97 test example  Obvious performance improvement from the original to the extended problem.

9. Analysis of Data about Slovenian Rivers Data  4 years (1990 - 1993)  Biological samples are taken twice a year (summer, winter).  Physical and chemical analyses are performed several times a year for each sampling site.  698 water examples  training (70% - 489 cases), test (30% - 209 cases)

10. The Influence of Physical and Chemical Parameters on Selected Organisms  From an ecological and water quality of view, these are important research topic.  Binary Classification: Present / Absent  Attributes  Plants: Hardness, NO2, NO3, NH4, PO4, SiO2, Fe, Detergents, COD, BOD  Animals: Temperature, PH, O2, Saturation, COD, BOD

12. Result  Accuracy: 66% - 85%  Information score: 23% - 50%  10 - 20 rules for each taxa  The average rule length was less than 5 conditions.  Average rule coverage was 15 to 45 examples.

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14. Biological Classification  13 physical and chemical parameters  27 bioindicators  7 classes  The majority class comprises 339 of the 698 examples, thus the default accuracy is 48.6%.

16. Discussion We have described several applications of rule induction in the domain of biological water quality classification.  The produced rules are transparent and can be easily understood by experts.  The induced rule contained valuable knowledge about the domain studied.  Machine learning techniques can be useful tools for classification and data analysis in the domain of river water quality and other ecological domains.