Trouble with classification using j48 algorithm in weka

Question

I'm trying to use the J48 classifier in Weka, but it classified everything as 0.

This is my DataSet:

 @relation 'SimpleRules-weka.filters.unsupervised.attribute.Reorder-R1,2,3,4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,6-weka.filters.unsupervised.attribute.Remove-R1-weka.filters.unsupervised.attribute.Remove-R1-weka.filters.unsupervised.attribute.Remove-R3-weka.filters.unsupervised.attribute.Remove-R1-2'

@attribute R1 numeric
@attribute R2 numeric
@attribute R3 numeric
@attribute R4 numeric
@attribute R5 numeric
@attribute R6 numeric
@attribute R7 numeric
@attribute R8 numeric
@attribute R9 numeric
@attribute Rank numeric
@attribute R1R5R6 numeric
@attribute R1R6R7 numeric
@attribute CombinedRules numeric
@attribute Demoday {0,1}

@data
1,1,1,1,1,0,1,1,0,11,12,0,0,0
1,1,1,1,0,1,0,1,0,72,1,0,0,0
0,0,0,1,0,1,1,1,0,47,7,0,0,1
1,1,0,1,1,1,0,1,1,68,12,1,0,0
1,1,1,1,1,1,0,1,1,21,7,1,0,0
1,1,1,1,0,1,1,1,1,63,11,0,1,0
1,1,0,1,0,1,1,1,0,19,7,0,1,0
1,0,1,1,0,0,1,1,1,11,7,0,0,0
0,1,1,1,0,1,0,1,0,107,12,0,0,0
1,1,1,1,0,1,0,1,0,99,12,0,0,1
0,1,1,1,0,1,1,1,1,238,2,0,0,0
1,1,1,1,1,0,1,1,0,147,7,0,0,0
1,1,1,1,1,1,0,1,1,30,7,1,0,1
1,1,1,1,0,0,0,1,1,124,5,0,0,0
0,1,1,1,1,1,0,1,1,54,5,0,0,0
0,0,0,1,0,1,0,1,0,153,5,0,0,0
1,1,1,1,0,1,0,1,1,33,5,0,0,0
1,1,1,1,1,1,0,1,0,143,3,1,0,0
1,0,1,1,0,1,1,1,0,28,3,0,1,0
0,1,1,1,0,1,1,0,0,83,8,0,0,0
1,1,1,1,1,1,1,1,0,31,7,1,1,0
1,1,1,1,0,0,0,1,0,91,12,0,0,0
1,1,1,1,0,0,1,1,0,7,7,0,0,0
1,1,1,1,0,1,0,1,1,4,1,0,0,0
1,1,0,1,1,1,0,1,0,41,1,1,0,0
0,1,1,1,0,1,0,1,1,84,5,0,0,0
1,1,0,1,0,1,1,1,0,81,1,0,1,1
0,1,1,1,1,1,0,1,1,8,6,0,0,0
1,1,1,1,0,1,1,1,1,172,11,0,1,0
1,1,1,1,1,0,0,1,1,142,12,0,0,1
0,1,1,1,0,1,1,1,1,35,11,0,0,0
1,1,1,1,0,1,0,1,1,130,11,0,0,0
1,1,1,1,0,1,1,1,1,62,7,0,1,0
0,1,1,1,0,1,1,1,1,34,7,0,0,0
0,1,1,1,0,1,1,1,1,108,3,0,0,0
0,1,1,1,0,1,0,1,1,11,12,0,0,0
0,1,1,1,1,0,0,1,1,129,3,0,0,0
1,1,0,1,0,1,1,1,1,24,10,0,1,1
1,1,1,1,0,1,1,1,0,50,8,0,1,0
1,1,1,1,1,1,0,1,1,12,12,1,0,1
0,1,1,1,1,1,1,1,0,111,3,0,0,0
1,1,0,1,0,1,0,1,1,55,11,0,0,0
1,1,1,1,0,1,0,1,1,239,11,0,0,0
0,1,1,1,1,1,0,1,0,131,2,0,0,0
1,1,1,1,0,1,0,1,1,328,8,0,0,0
1,1,1,1,0,1,1,1,1,12,12,0,1,1
1,1,1,1,0,1,0,1,1,113,8,0,0,0
0,1,1,1,0,1,0,1,0,96,1,0,0,0
1,1,1,1,0,0,0,1,1,75,7,0,0,0
1,1,1,1,1,1,0,1,1,67,1,1,0,1
1,1,1,1,1,1,0,1,0,112,11,1,0,0
1,1,1,1,0,0,1,1,1,109,3,0,0,0
1,0,1,1,1,0,0,1,0,47,12,0,0,0
1,1,1,1,0,1,0,1,1,47,7,0,0,0
1,1,1,1,0,1,0,1,1,2,6,0,0,0
0,0,0,1,0,1,1,1,0,16,2,0,0,0
1,1,1,1,0,1,0,1,0,18,12,0,0,0
0,1,1,1,1,1,1,1,0,58,3,0,0,0
0,0,0,1,1,1,0,1,0,156,7,0,0,0
1,1,1,1,1,0,1,1,0,279,2,0,0,0
1,1,1,1,0,1,0,1,0,2,12,0,0,0
0,0,1,1,0,1,0,1,1,163,6,0,0,0
1,1,1,1,1,1,0,1,1,10,3,1,0,1
0,0,1,1,1,1,0,1,0,3,12,0,0,0
1,1,1,1,1,1,0,1,1,101,7,1,0,0
1,1,1,1,0,1,0,1,0,136,9,0,0,0
0,1,1,1,1,0,0,1,0,31,8,0,0,0
1,0,1,1,1,1,0,1,0,155,8,1,0,0
0,1,1,1,0,1,1,1,0,158,12,0,0,0
0,1,0,1,0,1,0,1,0,101,1,0,0,0
0,1,0,1,0,1,0,1,1,7,7,0,0,0
1,0,0,1,1,1,0,1,0,23,1,1,0,0
1,0,0,1,1,0,0,1,1,99,1,0,0,0
1,1,1,1,1,1,0,1,1,73,3,1,0,0
1,1,1,1,1,1,0,1,0,15,3,1,0,0
1,1,1,1,0,1,1,1,0,97,8,0,1,0
1,1,1,1,0,1,1,1,1,93,8,0,1,0
1,1,1,1,1,1,1,1,0,44,7,1,1,1
0,1,1,1,0,1,0,1,0,239,7,0,0,0
0,0,0,1,1,1,0,1,1,35,1,0,0,0
0,1,1,1,0,1,0,1,0,90,12,0,0,0
1,1,1,1,1,1,0,1,1,37,7,1,0,0
1,1,1,1,1,0,0,1,1,25,12,0,0,1
1,1,1,1,0,0,0,1,0,83,2,0,0,0
1,1,1,1,1,1,1,1,1,22,10,1,1,1
1,1,1,1,1,0,1,1,1,2,10,0,0,0
1,0,1,1,0,1,1,1,1,65,5,0,1,0
0,1,1,1,0,1,1,1,1,25,3,0,0,0
1,0,1,1,0,0,1,1,0,180,8,0,0,0
0,1,0,1,0,1,1,1,1,49,10,0,0,0
0,0,1,1,0,1,0,1,0,67,8,0,0,0
1,1,1,1,1,0,1,1,0,14,11,0,0,0
1,0,0,1,1,1,0,1,0,36,11,1,0,0
0,0,0,1,0,0,1,1,1,97,9,0,0,0
0,0,0,1,0,1,1,1,0,193,1,0,0,0
0,0,1,1,1,1,1,1,1,83,6,0,0,0
0,1,1,1,0,1,0,1,1,13,12,0,0,0
1,1,1,1,0,1,0,1,0,49,5,0,0,0
1,0,1,1,1,1,1,1,1,1,8,1,1,1
1,0,1,1,0,1,0,1,1,159,10,0,0,0
1,1,1,1,1,1,1,1,0,51,7,1,1,0
1,1,1,1,1,1,0,1,1,168,6,1,0,0
0,1,1,1,0,1,0,1,1,100,5,0,0,0
0,0,0,1,0,0,1,1,0,30,3,0,0,0
1,1,0,1,0,1,1,1,0,27,12,0,1,0
1,1,1,1,0,1,0,1,1,34,11,0,0,0
0,1,0,1,0,1,1,1,1,101,3,0,0,0
1,0,1,1,0,1,0,1,1,111,11,0,0,0
1,1,1,1,1,1,0,1,0,51,2,1,0,0
1,1,1,1,0,0,0,1,0,233,12,0,0,0
1,1,1,1,1,0,0,1,1,98,11,0,0,0
0,1,1,1,0,1,0,1,0,24,1,0,0,0
1,1,1,1,0,0,1,1,1,181,2,0,0,0
1,1,1,1,1,1,0,1,1,14,6,1,0,0
0,1,1,1,1,1,1,1,1,96,1,0,0,0
1,1,1,1,0,1,1,1,1,139,12,0,1,1
1,1,1,1,1,1,1,1,1,155,8,1,1,0
1,1,1,1,1,1,0,1,0,53,7,1,0,1
0,1,1,1,0,1,1,1,0,17,8,0,0,0
1,1,1,1,0,1,1,1,0,39,6,0,1,0
0,0,1,1,0,1,0,1,0,282,12,0,0,0
1,0,1,1,1,0,0,1,1,132,7,0,0,0
1,1,1,1,0,0,0,1,0,57,11,0,0,0
1,0,0,1,0,1,1,1,1,165,7,0,1,1
0,1,0,1,0,1,1,1,1,74,10,0,0,0
0,1,1,1,0,1,1,1,0,150,7,0,0,0
1,0,1,1,1,1,0,1,1,53,2,1,0,0
1,1,1,1,1,1,0,1,1,42,12,1,0,1
1,1,1,1,1,0,1,1,1,234,7,0,0,0
1,1,1,1,0,0,1,1,1,164,10,0,0,0
1,1,1,1,0,0,0,1,0,69,3,0,0,0
1,1,1,1,0,0,1,1,0,38,5,0,0,0
1,0,0,1,0,1,0,1,1,56,7,0,0,0
1,1,0,1,0,1,0,1,1,63,1,0,0,0
1,1,1,1,1,1,1,1,0,9,1,1,1,0
1,0,1,1,0,1,0,1,1,23,11,0,0,0
1,1,1,1,1,0,0,1,0,46,7,0,0,0
1,1,1,1,0,0,0,1,1,59,12,0,0,0
1,1,0,1,0,1,0,1,1,27,1,0,0,0
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1,1,0,1,0,1,0,1,0,132,12,0,0,0
1,1,0,1,1,1,1,1,1,78,5,1,1,0
1,1,1,1,0,1,1,1,1,32,12,0,1,0
0,1,1,1,1,1,0,1,0,104,7,0,0,0
1,1,1,1,0,1,1,1,0,117,12,0,1,0
0,1,0,1,0,1,0,1,1,185,7,0,0,0
1,1,0,1,0,1,1,1,0,38,4,0,1,0
1,1,0,1,1,0,1,1,1,8,12,0,0,0
0,1,1,1,1,1,1,1,0,80,4,0,0,1
1,0,0,1,1,0,1,1,0,12,11,0,0,0
0,0,1,1,0,1,1,1,0,70,12,0,0,0
1,1,1,1,1,0,0,1,1,76,3,0,0,0
0,1,1,1,0,1,1,1,0,23,11,0,0,0
1,1,0,1,1,1,0,1,0,40,7,1,0,0
1,1,1,1,0,0,1,1,1,159,12,0,0,0
1,1,1,1,0,1,0,1,0,49,12,0,0,1
0,0,1,1,0,1,1,1,1,37,7,0,0,1
1,1,0,1,0,1,1,1,1,147,9,0,1,0
1,1,1,1,0,0,0,1,0,87,3,0,0,0
1,1,1,1,1,1,0,1,0,7,1,1,0,0
0,0,1,1,0,1,1,1,0,167,3,0,0,0
0,1,1,1,0,1,1,1,0,6,3,0,0,0
0,1,1,1,1,1,0,1,0,39,7,0,0,0
1,1,1,1,1,0,0,1,1,88,11,0,0,0
0,0,1,1,1,1,1,1,0,175,12,0,0,0
1,1,1,0,0,1,0,1,0,127,12,0,0,0
1,1,1,1,0,1,1,1,0,1,11,0,1,0
1,1,1,1,0,0,0,1,1,77,7,0,0,0
1,1,1,1,1,0,0,1,1,122,5,0,0,0
1,0,1,1,0,1,1,1,0,155,8,0,1,1
1,1,0,0,0,1,1,1,1,114,4,0,1,0
0,1,1,1,1,0,0,1,0,106,7,0,0,1
1,1,1,1,1,1,1,1,0,16,7,1,1,1
1,0,0,1,0,1,0,1,0,176,6,0,0,0
1,0,1,1,0,0,1,1,1,47,2,0,0,0
0,0,0,1,0,1,0,1,0,95,6,0,0,0
1,1,1,1,0,1,1,1,0,233,11,0,1,0
1,1,1,1,0,1,1,1,0,27,1,0,1,0
1,1,1,1,0,1,0,1,1,85,8,0,0,1
0,0,0,0,0,1,0,1,1,58,3,0,0,0
1,0,1,1,1,1,0,1,1,102,11,1,0,0
1,1,1,1,1,0,0,1,1,33,12,0,0,0
0,1,1,1,0,1,0,1,0,92,12,0,0,0
1,0,1,1,1,1,0,1,0,20,5,1,0,0
1,1,1,1,1,1,1,1,1,8,8,1,1,1
1,1,1,1,1,1,1,1,1,3,12,1,1,0
1,1,0,1,0,1,1,1,0,16,12,0,1,0
1,1,1,1,0,1,1,1,0,143,12,0,1,0
1,1,0,1,0,1,0,1,1,84,3,0,0,0
1,1,1,1,0,1,1,1,1,149,7,0,1,0
1,1,1,1,0,0,1,1,0,14,3,0,0,0
1,0,1,1,0,1,1,1,0,37,9,0,1,0
0,1,1,1,0,0,0,1,1,137,1,0,0,0
1,0,1,1,0,1,0,1,1,121,1,0,0,0
1,0,0,1,0,1,1,1,1,21,3,0,1,0
1,1,1,1,1,0,1,1,1,23,5,0,0,0
1,0,1,1,0,1,1,1,0,40,11,0,1,0
1,1,1,1,0,1,1,1,1,82,6,0,1,1
1,1,1,1,0,0,1,1,1,106,12,0,0,0
0,0,1,1,1,0,0,1,1,62,7,0,0,0
1,1,1,1,0,1,0,1,0,90,1,0,0,0
1,1,1,1,0,1,1,1,0,26,12,0,1,1
0,1,1,1,0,1,1,1,0,49,11,0,0,0
0,1,1,1,0,1,0,1,1,67,7,0,0,0
1,1,1,1,0,0,1,1,1,120,3,0,0,0
1,1,1,1,1,1,1,1,0,92,1,1,1,0
1,1,0,1,1,1,0,1,0,22,5,1,0,1
1,1,1,1,0,0,1,1,0,130,1,0,0,0
1,1,1,1,0,1,1,1,1,135,3,0,1,0
1,1,0,1,0,1,0,1,1,94,6,0,0,0
0,1,1,1,1,0,0,1,0,63,3,0,0,0
1,1,1,1,0,1,1,1,1,40,3,0,1,0
1,1,1,1,0,1,0,1,1,512,12,0,0,0
1,1,0,1,0,1,0,1,1,60,10,0,0,1
0,0,0,1,0,0,1,1,0,154,11,0,0,0
1,1,1,1,1,0,1,1,0,117,3,0,0,1
1,1,1,1,1,1,0,1,1,198,3,1,0,0
1,0,1,1,1,1,1,1,0,51,2,1,1,0
1,0,0,1,1,0,1,1,1,53,1,0,0,0
1,1,0,1,0,1,1,1,0,115,12,0,1,0
1,1,1,1,1,0,0,1,1,86,1,0,0,0
1,1,1,1,1,1,1,1,0,65,5,1,1,0
0,1,1,1,1,1,1,1,1,51,6,0,0,0
1,1,1,1,0,0,0,1,0,41,2,0,0,0
1,1,1,1,0,1,1,1,1,104,3,0,1,0
0,1,1,1,1,0,0,1,0,44,9,0,0,0
1,0,0,1,1,0,0,1,1,145,2,0,0,0
1,1,1,1,0,1,1,1,0,199,10,0,1,1
1,1,0,1,0,1,1,1,1,3,3,0,1,0
1,1,1,1,0,0,0,1,0,10,5,0,0,0
1,1,1,1,1,1,0,1,1,81,7,1,0,0
1,1,0,0,0,1,0,1,0,164,6,0,0,0
0,1,1,1,0,1,1,1,1,122,5,0,0,0
1,1,1,1,1,1,0,1,1,188,3,1,0,0
1,1,1,1,0,0,0,1,1,149,5,0,0,0
1,1,1,1,0,0,0,1,1,152,12,0,0,0
1,1,1,1,0,1,1,1,1,5,10,0,1,0
1,0,1,1,1,0,0,1,0,35,5,0,0,0
1,1,1,1,0,0,1,1,1,12,4,0,0,0

And here are the results after running the j48 algorithm with 10 folds cross validation

Correctly Classified Instances         205               85.7741 %
Incorrectly Classified Instances        34               14.2259 %
Kappa statistic                          0.0266
Mean absolute error                      0.2346
Root mean squared error                  0.3465
Relative absolute error                100.0672 %
Root relative squared error            101.7226 %
Coverage of cases (0.95 level)          99.5816 %
Mean rel. region size (0.95 level)      99.3724 %
Total Number of Instances              239     

=== Detailed Accuracy By Class ===

                 TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                 0,986    0,969    0,868      0,986    0,923      0,044    0,492     0,865     0
                 0,031    0,014    0,250      0,031    0,056      0,044    0,492     0,135     1
Weighted Avg.    0,858    0,841    0,785      0,858    0,807      0,044    0,492     0,767     

=== Confusion Matrix ===

   a   b   <-- classified as
 204   3 |   a = 0
  31   1 |   b = 1

And the tree that it generates its this https://www.dropbox.com/s/qzjukr8klffwl90/Captura%20de%20pantalla%202014-04-15%2022.33.38.png

I hope you could help me to solve this issue,

Thanks a lot,

Answer 1

It is classical class imbalance problem. Have a look on your class distribution Demoday=1 32 records, Demoday = 0 207 records. Almost every machine learning algorithm is designed to achieve best overall accuracy. So, in your case, if it assigns 0 to every instance it yields 85,7% accuracy and this is exactly the one-leaf tree you get. The problem is of course, that the minority class is usually of greater interest. Google unbalanced class, or class imbalance for more info. I have already posted some quick solutions as well as some model performance indicators suitable for this case as answer here: how to edit weka configurations to find "1"

Good luck