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Active Rare Class Discovery and Classification Using Dirichlet Processes

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Classification is used to solve countless problems. Many real world computer vision problems, such as visual surveillance, contain uninteresting but common classes alongside interesting but rare classes. The rare classes are often unknown, and need to be discovered whilst training a classifier. Given a data set active learning selects the members within it to be labelled for the purpose of constructing a classifier, optimising the choice to get the best classifier for the least amount of effort. We propose an active learning method for scenarios with unknown, rare classes, where the problems of classification and rare class discovery need to be tackled jointly. By assuming a non-parametric prior on the data the goals of new class discovery and classification refinement are automatically balanced, without any tunable parameters. The ability to work with any specific classifier is maintained, so it may be used with the technique most appropriate for the problem at hand. Results are provided for a large variety of problems, demonstrating superior performance.

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  1. An implementation is available from

  2. Sometimes referred to as passive learning.

  3. It is not really solvable after this, as the classes have a lot of overlap, but it is sufficient to illustrate the inner workings of the presented approach, whilst reducing it to \(1D\) allows for a clean visualisation.

  4. We set this classifier parameter to 32.

  5. A density estimate that we hallucinate is the prior for the classification algorithm.

  6. Note that concentration cannot be calculated until at least two classes have been found, hence the jump in the graph at that time.

  7. 32 in all cases except for kdd99 and faces, where it is 24 and 16, respectively due to their size.

  8. Balanced inlier rate is calculated as the average inlier rate for each class in the training set. Inlier rate is the number of correct classifications divided by the number of exemplars being classified. This can be interpreted as recall generalised to 3+ classes.

  9. Note that culling is for the entire data set, whilst separation into training and testing was purely random, so classes can have \(<\)10 entries in the pool.

  10. Whilst this strategy can always beat the presented approach it does so by introducing a scale parameter, which has to be selected for each problem. This is inappropriate, as doing multiple runs to find the best parameter obviates the entire purpose of active learning.

  11. There is even an interesting human interface issue of presenting such priors to a non-expert, such that they can communicate what they already know about a specific problem.

  12. With KDE this is obtained by training several classifiers on bootstrap samples from the training set. This achieves the goal of measuring model variance, but damages performance, so a fully trained version is kept to do actual classification.


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Correspondence to Tom S. F. Haines.

Appendix: Alternative Choices

Appendix: Alternative Choices

We now discuss some of the alternatives to the presented approach that were considered. Firstly, as discussed in Sect. 3.3, one variant lead to an improvement, specifically soft selection over hard selection. Soft selection can be taken further—a parameter can be introduced as a power of the \(P(\mathrm{wrong })\) value, to emphasis or de-emphasis large values. This can be tuned to get better results, but as a problem specific parameter it is of no value to active learning, as parameter tuning is incompatible with a single set of queries. The KDE variant in Fig. 6 is similar, except its parameter is fatally sensitive.

The probability of being wrong can be interpreted as an expectation over zero-one loss—alternative loss functions can be considered. Hinge loss for a multinomial distribution can be defined as the difference between the probability of the correct answer and the highest probability in the distribution, which is 0 if the correct answer has the greatest probability. It often undermined performance however.

Query by committee (QBC) was explored by Loy et al. (2012); however, their formulation really served as a probabilistic selection threshold function. Using multiple models it can be formulated to measure variance, so that \(P(\mathrm{wrong })\) also focuses on areas with high model uncertainty.Footnote 12 Noting that there are two estimates—an estimate of what the actual class membership is, including the possibility of being something new, and an estimate of what the classifier is going to assign, we can use different models from a committee for these two roles. A QBC variant can then be defined using a committee where all assignment combinations are summed out, so a high QBC \(P(\mathrm{wrong })\) score is obtained at boundaries between classes, in areas where new classes could be found, and where the current model has high uncertainty. This unfortunately resulted in too much emphasis being placed on boundary refinement.

For some problems the above variants are better. The issue is there is no way to predict which problems in advance, and for some problems they are much worse. Active learning is a scenario where you choose a method and apply it to your problem once—multiple runs require that the queries for each be satisfied, which is contrary to the goal. We therefore present \(P(\mathrm{wrong })\) as formulated, as it is consistent—it never performs poorly, and usually gives top tier performance. Future work could consider inferring which data sets work best with different active learners.

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Haines, T.S.F., Xiang, T. Active Rare Class Discovery and Classification Using Dirichlet Processes. Int J Comput Vis 106, 315–331 (2014).

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