Help about Accuracy, ROC Area, TPR, FPR, Precision Between three Classifiers

I made three classifications in Weka 3.6 by using same dataset and arff file. I chose classifiers as: NaiveBayes, J48 and SMO. I have a problem/misunderstanding about outputs.

Bayes had the lowest accuracy but highest ROC area, J48 had lower ROC Area than Bayes but highest ACC. Are not suppose to be the classifier which have highest ACC also have highest ROC Area?

Secondly, Bayes had the lowest TP Rate but also had the lowest FP Rate and highest Presicion for the class b. In addition SMO had the highest TPR for class b and also had the highest FPR. How can it happen? Could you please help me about this results? My classifier outputs are below:

Bayes:

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 661 91.8056 %

Incorrectly Classified Instances 59 8.1944 %

Kappa statistic 0.8361

Mean absolute error 0.0881

Root mean squared error 0.2805

Relative absolute error 17.6262 %

Root relative squared error 56.0991 %

Total Number of Instances 720

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class

0.994 0.158 0.863 0.994 0.924 0.978 a

0.842 0.006 0.993 0.842 0.911 0.978 b

Weighted Avg. 0.918 0.082 0.928 0.918 0.918 0.978

J48:

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 683 94.8611 %

Incorrectly Classified Instances 37 5.1389 %

Kappa statistic 0.8972

Mean absolute error 0.0746

Root mean squared error 0.2165

Relative absolute error 14.9276 %

Root relative squared error 43.2941 %

Total Number of Instances 720

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class

0.975 0.078 0.926 0.975 0.95 0.973 a

0.922 0.025 0.974 0.922 0.947 0.973 b

Weighted Avg. 0.949 0.051 0.95 0.949 0.949 0.973

SMO:

=== Stratified cross-validation ===

=== Summary ===

Correctly Classified Instances 680 94.4444 %

Incorrectly Classified Instances 40 5.5556 %

Kappa statistic 0.8889

Mean absolute error 0.0556

Root mean squared error 0.2357

Relative absolute error 11.1111 %

Root relative squared error 47.1405 %

Total Number of Instances 720

=== Detailed Accuracy By Class ===

TP Rate FP Rate Precision Recall F-Measure ROC Area Class

0.953 0.064 0.937 0.953 0.945 0.944 a

0.936 0.047 0.952 0.936 0.944 0.944 b

Weighted Avg. 0.944 0.056 0.945 0.944 0.944 0.944