A method for identifying highly-skewed classes using an imperfect annotation of every instance together with a set of features for all instances. The imperfect annotations designate a plurality of instances as belonging to the target rare class and others to the majority class. First, a classifier is trained on the set of features using the imperfect annotation as supervision, to designate each instance to either the rare class or majority class. A combination of the predictions from the trained classifier and the imperfect annotations is then used to classify each instance to either the rare class or majority class. In particular, an instance is classified to the rare class only when both the trained classifier and the imperfect annotation classify the instance to the rare class. Finally, for each instance assigned as a rare class instance by the combination stage, all instances in its neighborhood are re-classified as either rare class or majority class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of identifying which items in a collection of items belong to a rare class, the method comprising: training a classifier based on annotations of data by a heuristic rule, the annotations designating a plurality of items as being either in a rare class or a majority class; using a combination of the trained classifier and a second classifier to classify a further plurality of items based on data, wherein an item is classified in the rare class by the combination of the trained classifier and the second classifier only when both the trained classifier and the second classifier classify the item in the rare class; and for each item classified in the rare class by the combination of the trained classifier and the second classifier, reclassifying all items that are connected to the item in the rare class in either the rare class or the majority class. 2. The method of claim 1 wherein the second classifier comprises the heuristic rule used to form annotations for training the trained classifier. 3. The method of claim 1 wherein the ratio of the number of items in the majority class to the number of items in the rare class in the data exceeds one hundred. 4. The method of claim 1 wherein using the combination of the trained classifier and second classifier comprises using the second classifier to classify all of the further plurality of items and using the trained classifier to classify only those items classified in the rare class by the second classifier. 5. The method of claim 4 wherein the second classifier requires less computations per classification than the trained classifier. 6. The method of claim 1 wherein the trained classifier is trained without knowing whether items designated as being in the rare class by the heuristic rule are truly in the rare class. 7. The method of claim 1 further comprising dividing the data into homogenous sets based on geography and land cover, wherein training a classifier comprises training a separate classifier for each homogenous set. 8. The method of claim 7 wherein classifying a further plurality of items comprises for each item determining attributes for the item and classifying the item using the classifier trained for a homogenous set containing items with similar attributes. 9. A method of improving computer-based classification of highly skewed data, the method comprising: receiving highly skewed data comprising members that are in either a first class or a second class wherein the data is highly skewed because the ratio of the number of members in the first class to the number of members in the second class is greater than one hundred; classifying each member in the highly skewed data using two separate classifiers such that a member is classified in the second class only if both classifiers place the member in the second class; identifying connections between the members; and for each member connected to a member classified in the second class, reclassifying the connected member as either being in the first class or the second class. 10. The method of claim 9 wherein receiving the highly skewed data comprises receiving the highly skewed data without an indication of the true class of any of the members in the data. 11. The method of claim 9 wherein the members comprise people and the classes comprise sick people and healthy people. 12. The method of claim 11 wherein identifying connections between the members comprises identifying people who have come in contact with each other. 13. The method of claim 9 wherein the members comprise network activities and the classes comprise security breaches and normal network traffic. 14. The method of claim 9 further comprising: applying a heuristic to the highly skewed data to generate a weak class label for all the members; and using the weak class labels for the members in the set of members to train one of the two separate classifiers. 15. The method of claim 14 wherein the other of the two separate classifiers comprises the heuristic. 16. The method of claim 13 wherein classifying each member in the highly skewed data using two separate classifiers comprises classifying each member using the heuristic and using the trained classifier to classify only those members classified in the second class by the heuristic. 17. The method of claim 9 wherein each member comprises an area on the globe, the first class comprises unburned areas and the second class comprises burned areas. 18. A computing device comprising: a memory storing values for a plurality of objects; a processor performing steps comprising: assigning imperfect labels to at least some of the plurality of objects; training a classifier based on the imperfect labels; classifying each object using the trained classifier and a second classifier; for each object classified in a first class, identifying a connected object and reclassifying the connected object. 19. The computing device of claim 18 wherein classifying each object using the trained classifier and the second classifier comprises classifying an object in the first class only if both the trained classifier and the second classifier classify the object in the first class. 20. The computing device of claim 18 wherein the second classifier comprises a heuristic that assigns the imperfect labels to the objects. 21. The computing device of claim 18 wherein the plurality of objects comprise objects of the first class and objects of a second class and wherein the ratio of the number of objects of the second class to the number of objects of the first class is greater than one hundred.