Machines, systems and methods for maintaining a representative data set in a document classification system, the method comprising: including an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of the representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; updating the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system; and retraining the KB, wherein the retraining is performed based on occurrence of one or more events.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for maintaining a representative data set in a document classification system, the method comprising: including an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; updating the RDS by adding or removing representative data from the RIDS based on feedback received about accuracy of classification of one or more documents by the classification system; and retraining the KB, wherein the retraining is performed based on occurrence of one or more events. 2. The method of claim 1 wherein the one or more events comprise passage of a certain period of time. 3. The method of claim 1 wherein the one or more events comprise receiving a certain number of feedbacks. 4. The method of claim 1 wherein the one or more events comprise adopting a certain number of feedbacks b adding or deleting one or more representative data from the RDS. 5. The method of claim 1 wherein the one or more events comprise both receiving N feedbacks and adding at least X representative data to the RDS as a result of receiving the N feedbacks. 6. The method of claim 1 wherein the representative data is associated with one or more classes in the plurality of classes, the method further comprising updating the RDS such that number of classes with which the representative data is associated and the number of representative data in each class is adjusted to maintain a balanced inclusion of representative data in each class. 7. The method of claim 6 wherein the updating of the RDS is performed, in response to determining that a number of representative data associated with a first class from among said plurality of classes is above a first threshold, 8. The method of claim 6 wherein the updating of the RDS is performed, in response to determining that a number of representative data associated with a first class from among said plurality of classes is below a second threshold. 9. The method of claim 6 wherein the updating of the RDS is performed, in response to determining that a number of representative data in the RDS has exceeded a third threshold. 10. The method of claim 6 wherein the updating of the RDS is performed, in response to determining that a number of representative data in the RDS has fallen below a fourth threshold.