GENERATING NEGATIVE CLASSIFIER DATA BASED ON POSITIVE CLASSIFIER DATA
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.
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.