In one-vs-all approach, you have to check for all 5 models. Then you can take the decision with the most confidence value. LIBSVM
gives probability estimates.
In one-vs-one approach, you can take the majority. For example, you test 1 vs. 2, 1 vs. 3, 1 vs. 4 and 1 vs. 5. You classify it as 1 in 3 cases. You do the same for other 4 classes. Suppose for other four classes the values are [0, 1, 1, 2]
. Therefore, class 1 was obtained most number of times, making that class as the final class. In this case, you could also do total of probability estimates. Take the maximum. That would work unless in one pair the classification goes extremely wrong. For example, in 1 vs. 4, it classifies 4 (true class is 1) with a confidence 0.7. Then just because of this one decision, your total of probability estimates may shoot up and give wrong results. This issue can be examined experimentally.
LIBSVM uses one vs. one. You can check the reasoning here. You can read this paper too where they defend one vs. all classification approach and conclude that it is not necessarily worse than one vs. one.