An Online Classification and Prediction Hybrid System for Knowledge Discovery in Databases

Expert Systems in data mining generally use knowledge extraction methods to form a classifier or predictor. These have the advantage of forming high quality results due to the inclusion of expert knowledge. The problem, however, is that they do not allow for autonomous knowledge discovery. Therefore, such systems will only find results that the expert is capable of giving examples about and will not find unknown patterns. Thus, Knowledge Discovery in Database (KDD) generally relies on other machine learning tools, forgoing the advantage of expert knowledge. This paper presents a method for incrementally building a knowledge base using expert knowledge in such a way that potentially the system is still able to autonomously discover unknown patterns. It does this through prudence checking of the knowledge base for each case presented and informs the expert when the knowledge base has an inconsistency needing clarification. Initial results show strong potential for the system in identifying misclassifications allowing its potential application to KDD.