Table 4  Accuracy for difference classification methods
Method  a  DT  RF  KNN  SVM  NB
All data1:
ARBC  b  0.924  0.968  0.943  0.999  0.476
CW AR  c  0.926  0.971  0.978  0.999  0.531
UR  d  0.873  0.933  0.893  0.970  0.519
No SSE data2:
ARBC_WO_SSE  e  0.917  0.951  0.936  0.992  0.451
CW AR_WO_SSE  f  0.927  0.970  0.979  0.988  0.492
UR_WO_SSE  g  0.800  0.850  0.800  0.890  0.483
aMethod represents different classification methods such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor(KNN), Support Vector Machine (SVM) and Naive Bayes (NB);
bARBC: Association rule based classification;
cCW AR: Classification based on physicochemical properties;
dUR: ARBC classification using 58 unique association rules;
e, f, g: Data sets with exclusion of SSE content from All data1;
1All data: Data sets including SSE content;
2No SSE data: Data sets without inclusion of SSE content.