Increasingly, machine learning is being employed for automatically classifying data. Often data may be classified by associating it with one or more labels that correspond to various features of the data. For example, an image classifiers may be arranged to classify images of animals based on the type of animal shown in a given image. In this example, labels may be defined for the type of animal (e.g., sheep, tiger, whale, and so on). Also, in some cases, labels may be defined for various properties of data. Going back to the animal example, labels may be defined such as, mammal, reptile, bird, carnivore, or the like. In many cases, machine learning systems used for classifying data need to be trained using sample data that is correctly labeled. If the classifier is trained using sufficiently and correctly labeled sample data, it may be enabled to correctly classify provided data based on its training. In some cases, many correctly labeled sample data examples must be provided to properly train the classifier. Labeling the sample data may require an expert or other user to manually assign labels to each sample data. In some cases, it may be very time consuming and/or labor intensive to generate the large number of label sample data examples that may be required to train a classifier. Thus, it is with respect to these considerations and others that the invention has been made.