SIMPLE
Figure 11: Left: Data Set ‘Twonorm2D’ that was created by projecting the UCI ‘Twonorm’ data set to two dimensions. This data set demonstrates the unstable general behavior of active learners during early stages of the learning session. Right: Learning curves ofSIMPLE
and enhancedSIMPLE(via buffering) on the ‘Twonorm2D’ data set.
We propose the following solution using the idea of “buffering”, as well as our classification entropy estimator. One of the symptoms of the drastic decision boundary instability described above is a (drastic) change in the classification entropy. We observe that when the boundary shifts from the border between the two clusters to the middle of one of the clusters (iterations 5,7) the entropy decreases. Therefore, the solution we propose is to look at the change in entropy of the pool (and training set) classification in the end of each trial. As long as the entropy decreases, new examples are buffered and not added to the training set. Once the entropy has not decreased, all buffered
(4) (5) (6)
(7) (8)
Figure 12: Pool classification and training sets (blackened points) generated by theSIMPLEactive learner after 4-8 iterations on the ‘Twonorm2D’ data set.
points are added to the training set. If we want to keep a bounded buffer this solution introduces a new parameter; namely, the buffer size. In our experience a small buffer is sufficient and in all our experiments we used a buffer of three points.
This buffering approach provides an effective solution to the above problem. In Figure 11 (right) we see the learning curves of SIMPLEon the ‘Twonorm2D’ data set with and without this
enhancement. It is evident that the learning curve of the original algorithm is very unstable. Adding the buffering enhancement smoothes the learning curve. Figure 13 shows the pool classification and the training sets generated by the enhancedSIMPLEactive learner after 4-8 iterations. The decision
boundary now does not change drastically from one iteration to another.
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(4) (5) (6)
(7) (8)
Figure 13: Pool classification and training sets (blackened points) generated by the enhanced
SIMPLEactive learner after 4-8 iterations on the ‘Twonorm2D’ data set.
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