Examples relate to generating negative classifier data based on positive classifier data. In one example, a computing device may: obtain positive classifier data for a first class, the positive classifier data including at least one correlated feature set and, for each correlated feature set, a measure of likelihood that data matching the correlated feature set belongs to the first class; determine, for each feature included in the at least one correlated feature set, a de-correlated measure of likelihood that data including the feature belongs to the first class; and generate, based on each de-correlated measure of likelihood, negative classifier data for classifying data as belonging to a second class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory machine-readable storage medium encoded with instructions executable by a hardware processor of a computing device for generating negative classifier data based on positive classifier data, the machine-readable storage medium comprising instructions to cause the hardware processor to: obtain positive classifier data for a first class, the positive classifier data including at least one correlated feature set and, for each correlated feature set, a measure of likelihood that data matching the correlated feature set belongs to the first class; determine, for each feature included in the at least one correlated feature set, a de-correlated measure of likelihood that data including the feature belongs to the first class; and generate, based on each de-correlated measure of likelihood, negative classifier data for classifying data as belonging to a second class. 2. The storage medium of claim 1, wherein each de-correlated measure of likelihood is determined, for each feature included in the at least one correlated feature set, by calculating a sum of each likelihood that the feature would be randomly selected from each of its corresponding feature sets. 3. The storage medium of claim 1, wherein the instructions further cause the hardware processor to: train a classifier based on the positive classifier data and the negative classifier data. 4. The storage medium of claim 3, wherein the classifier receives, as input, test data including at least one feature value and produces, as output, an output class for the test data. 5. The storage medium of claim 1, wherein each correlated feature set is correlated with respect to an order of feature values. 6. A computing device for generating negative classifier data based on positive classifier data, the computing device comprising: a hardware processor; and a data storage device storing instructions that, when executed by the hardware processor, cause the hardware processor to: obtain positive classifier data for a first class, the positive classifier data including at least one correlated feature set and, for each feature set, a measure of likelihood that data matching the feature set belongs to the first class; determine, for each feature included in the at least one correlated feature set, a de-correlated measure of likelihood that data including the feature belongs to the first class; and generate, based on each de-correlated measure of likelihood, negative classifier data for classifying data as belonging to a second class. 7. The computing device of claim 6, wherein each de-correlated measure of likelihood is determined, for each feature included in the at least one correlated feature set, by calculating a sum of each likelihood that the feature would be randomly selected from each of its corresponding feature sets. 8. The computing device of claim 6, wherein the instructions further cause the hardware processor to: train a classifier based on the positive classifier data and the negative classifier data. 9. The computing device of claim 8, wherein the classifier receives, as input, test data including at least one feature value and produces, as output, an output class for the test data. 10. The computing device of claim 6, wherein each correlated feature set is correlated with respect to an order of feature values. 11. A method for generating negative classifier data based on positive classifier data, implemented by a hardware processor, the method comprising: obtaining positive classifier data for a first class, the positive classifier data including at least one correlated feature set and, for each feature set, a measure of likelihood that data matching the feature set belongs to the first class; determining, for each feature included in the at least one correlated feature set, a de-correlated measure of likelihood that data including the feature belongs to the first class; and generating, based on each de-correlated measure of likelihood, negative classifier data for classifying data as belonging to a second class. 12. The method of claim 11, wherein each de-correlated measure of likelihood is determined, for each feature included in the at least one correlated feature set, by calculating a sum of each likelihood that the feature would be randomly selected from each of its corresponding feature sets. 13. The method of claim 11, further comprising: training a classifier based on the positive classifier data and the negative classifier data. 14. The method of claim 13, wherein the classifier receives, as input, test data including at least one feature value and produces, as output, an output class for the test data. 15. The method of claim 11, wherein each correlated feature set is correlated with respect to an order of feature values.