Disclosed herein is a system and method that can be used with any underlying classification technique. The method receives a test dataset and determines the features in that test dataset that are present. From these features the training dataset is modified to only have those features that are present in the test dataset. This modified test dataset is then used to calibrate the classifier for the particular incoming data set. The process repeats itself for each different incoming dataset providing a just in time calibration of the classifier.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for training a data classifier comprising: a data analysis component configured to determine a first plurality of features present in a test data; a training dataset, the training dataset having a plurality of entries, each of the plurality of entries having a second plurality of features wherein the first plurality of features are a component of the second plurality of features; a training dataset generator configured to generate a modified training dataset from the training dataset wherein the modified training dataset reduces the second plurality of features to the only the first plurality of features; and a classifier configured to classify the test data based on the classifier being calibrated using the modified training set and not the training dataset. 2. The system of claim 1 wherein the training dataset generator is further configured to remove from the modified training dataset an entry when that entry has a missing feature from the first plurality of features. 3. The system of claim 1 wherein the training dataset generator is further configured to remove form the modified training dataset an entry when that entry has a missing feature from the first plurality of features that is determined to be an informative feature. 4. The system of claim 1 wherein the training dataset generator is further configured to not generate a modified training dataset when one feature of the second plurality of features not in the first plurality of features is determined to be an informative feature. 5. The system of claim 4 wherein the training dataset generator is configured to send a message back to an originator of the test data an indication that the test data is missing the informative feature and will not be further processed. 6. The system of claim 4 wherein the training dataset generator is configured to modify the test data by adding a value to the test data for the one feature missing from the test data. 7. The system of claim 1 further comprising: a calibrated classifier store, the calibrated classifier store storing at least one calibrated classifier that was previously generated using a second modified training dataset, the second modified training dataset different from the modified training dataset. 8. The system of claim 7 wherein the training dataset generator is further configured to prior to generating the modified dataset to determine if the at least calibrated classifier in the calibrated classifier store was generated based upon the first plurality of features and to cause the at least one calibrated classifier to be used as the classifier. 9. The system of claim 7 wherein the calibrated classifier store is configured to add the classifier to the calibrated classifier store following calibration using the modified training dataset. 10. A method for training a classifier the method comprising: receiving a test dataset; analyzing the test dataset to determine a plurality of features present in the test dataset; generating a modified training dataset from a training dataset based upon the determined plurality of features; and calibrating the classifier using the modified training dataset, wherein the preceding steps are performed by at least one processor. 11. The method of claim 10 further comprising: classifying the test dataset using the calibrated classifier. 12. The method of claim 10 wherein generating a modified training dataset further comprises for each entry of a plurality of entries in the training dataset: removing from a second plurality of features in the training dataset those features which are not in common with the determined plurality of features. 13. The method of claim 10 wherein generating a modified training dataset further comprises for each entry of a plurality of entries in the training dataset: removing from the training dataset entries from the plurality of entries that have at least one missing value for features in the determined plurality of features. 14. The method of claim 13 wherein removing the entry occurs when the at least one missing value is determined to be associated with an informative feature. 15. The method of claim 10 further comprising: storing the calibrated classifier in a calibrated classifier store. 16. The method of claim 15 wherein storing the calibrated classifier further comprises, removing a previously stored calibrated classifier from the calibrated classifier store. 17. The method of claim 10 wherein prior to generating the modified training dataset: determining if a previously generated calibrated classifier exists for the determined plurality of features; inserting the previously generated calibrated classifier as the classifier when it is determined the previously generated calibrated classifier matches the determined plurality of features; and not performing the steps of generating and calibrating. 18. The method of claim 10 wherein prior to generating the modified training dataset: determining if the test dataset has a missing feature that is an informative feature; and not performing the steps of generating and calibrating when the test dataset has a missing informative feature. 19. The method of claim 18 further comprising: sending a message to an originator of the test dataset indicating that the test dataset is missing an informative feature.