10. A computer program product for maintaining a representative data set in a document classification system, the computer program product comprising logic code embedded in a non-transitory computer readable storage medium, wherein execution of the logic code on a processor causes a processor to: include 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; update 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, wherein the representative data is associated with one or more classes in the plurality of classes; further update the RDS such that a 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: and retrain the KB, wherein the retraining is performed based on occurrence of one or more events.
10. A computer program product for maintaining a representative data set in a document classification system, the computer program product comprising logic code embedded in a non-transitory computer readable storage medium, wherein execution of the logic code on a processor causes a processor to: include 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; update 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, wherein the representative data is associated with one or more classes in the plurality of classes; further update the RDS such that a 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: and retrain the KB, wherein the retraining is performed based on occurrence of one or more events. 14. The computer program product of claim 10 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.