I run adoboostM1 with default parameters on diabetes data set of weka. I got following results.
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.933 0.578 0.776 0.933 0.847 0.429 0.844 0.917 tested_negative
0.422 0.067 0.745 0.422 0.538 0.429 0.844 0.696 tested_positive
Weighted Avg. 0.77 0.416 0.766 0.77 0.749 0.429 0.844 0.847
Notice that this TP Rate and FP rate is for each of your class values. Since I have two (2) values for class feature in this data set, I have two (2) lines.
Also notice that:
0.933 + 0.067 = 1
0.578 + 0.422 = 1
As you correctly pointed that TP rate + FP rate should be equal to one (1). So in your example: I assume that you have following class variable:
target {A,B}
TP Rate FP Rate
0.8 0.47 ..... for A
0.53 0.2 ..... for B