A method of generating a predictor to classify data includes: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data. The first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a predictor to classify data, the method comprising: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data, wherein the first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively. 2. The method of claim 1, wherein the current training data comprises a plurality of current samples. 3. The method of claim 2, wherein the combining comprises: for each current sample, concatenating the current sample with all the predictions generated from the corresponding current sample to generate a new sample; and combining the new samples to generate the new training data. 4. The method of claim 3, wherein each current sample comprises a plurality of first feature indicators that indicate whether one of a plurality of first features is present in or absent from the corresponding current sample. 5. The method of claim 4, wherein the predictions generated from the corresponding current sample are probabilities output by each of the first classifiers indicating whether the current sample is part of a given class. 6. The method of claim 5, wherein the concatenating of the current sample with all the predictions comprises concatenating the first features indictors of the current sample with second feature indicators that correspond to the probabilities of the current sample. 7. The method of claim 3, further comprising: for each new sample, performing a calculation on the predictions of the corresponding new sample to set a classifier label of the corresponding new sample. 8. The method of claim 7, wherein the calculation sets the classifier label to indicate the new sample is part of a given class when a majority of the predictions indicate the new sample is part of the given class and otherwise sets the classifier label to indicate the new sample is not part of the given class. 9. The method of claim 1, wherein the classifier types include support vector machine, neural network, and decision tree. 10. A computer system configured to generate a predictor to classify data, the system comprising: a memory storing a computer program; and a processor configured to execute the computer program, wherein the computer program is configured to train each of a plurality of first classifiers arranged in a first level on current training data, operate each classifier of the first level on the training data to generate a plurality of predictions, combine the current training data with the predictions to generated new training data, and train each of a plurality of second classifiers arranged in a second level on the new training data, wherein the first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively. 11. The computer system of claim 10, further comprising a database storing the current training data. 12. The computer system of claim 10, wherein the current training data comprises a plurality of current samples. 13. The computer system of claim 12, wherein the computer program combines the current training data with the predictions by concatenating, for each current sample, the current sample with all the predictions generated from the corresponding current sample to generate a new sample, and combining the new samples to generate the new training data. 14. The computer system of claim 13, wherein each current sample comprises a plurality of first feature indicators that indicate whether one of a plurality of first features is present in or absent from the corresponding current sample. 15. The computer system of claim 14, wherein the predictions generated from the corresponding current sample are probabilities output by each of the first classifiers indicating whether the current sample is part of a given class. 16. The computer system of claim 15, wherein the concatenating comprises concatenating the first features indictors of the current sample with second feature indicators that correspond to the probabilities of the current sample. 17. A computer program product configured to generate a predictor to classify data, the program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to perform a method comprising: training each of a plurality of first classifiers arranged in a first level on current training data; operating each classifier of the first level on the training data to generate a plurality of predictions; combining the current training data with the predictions to generated new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data, wherein the first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively. 18. The computer program product of claim 17, wherein the current training data comprises a plurality of current samples. 19. The computer program product of claim 18, wherein the combining comprises: for each current sample, concatenating the current sample with all the predictions generated from the corresponding current sample to generate a new sample; and combining the new samples to generate the new training data. 20. The computer program product of claim 19, wherein each current sample comprises a plurality of first feature indicators that indicate whether one of a plurality of first features is present in or absent from the corresponding current sample. 21. The computer program product of claim 20, wherein the predictions generated from the corresponding current sample are probabilities output by each of the first classifiers indicating whether the current sample is part of a given class. 22. A method of generating a predictor to classify data, the method comprising: splitting current training data into first and second training data; training each of a plurality of first classifiers arranged in a first level on the first training data; operating each classifier of the first level on the second training data to generate first fused data; training each classifier of the first level on the second training data; operating each classifier of the first level on the first training data to generate second fused data; combining the first fused data with the second fused data to generate new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data, wherein the first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively. 23. The method of claim 22, wherein the operating of the each classifier of the first level on the second training data to generate first fused data comprises: operating the first classifiers on the second training data to generate first predictions; combining the second training data with the first predictions to generate the first fused data. 24. The method of claim 23, wherein the operating of each classifier of the first level on the first training data to generate second fused data comprises: operating the first classifiers on the first training data to generate second predictions; combining the first training data with the second predictions to generate the second fused data. 25. The method of claim 24, wherein each of the predictions are probabilities indicating whether a given sample the corresponding training data is part of a given class. 26. A computer program product configured to generate a predictor to classify data, the program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to perform a method comprising: splitting current training data into first and second training data; training each of a plurality of first classifiers arranged in a first level on the first training data; operating each classifier of the first level on the second training data to generate first fused data; training each classifier of the first level on the second training data; operating each classifier of the first level on the first training data to generate second fused data; combining the first fused data with the second fused data to generate new training data; and training each of a plurality of second classifiers arranged in a second level on the new training data, wherein the first classifiers are classifiers of different classifier types, respectively and the second classifiers are classifiers of the different classifier types, respectively 27. The method of claim 26, wherein the operating of the each classifier of the first level on the second training data to generate first fused data comprises: operating the first classifiers on the second training data to generate first predictions; combining the second training data with the first predictions to generate the first fused data. 28. The method of claim 27, wherein the operating of each classifier of the first level on the first training data to generate second fused data comprises: operating the first classifiers on the first training data to generate second predictions; combining the first training data with the second predictions to generate the second fused data. 29. The method of claim 28, wherein each of the predictions are probabilities indicating whether a given sample the corresponding training data is part of a given class.