Interactive use of inductive approach for analyzing and developing conceptual structures

Abstract

Inductive learning algorithms learns classification from training examples and uses induced classifier for dealing with new instances. The use of conceptual data structures for classifier's input is making this task more complicated and classifier may meet the difficulties in class prediction. To broaden applicability of inductive learning based classifiers a collaborative approach between the system and human expert would be useful. The proposed interactive system in uncertain conditions can ask for human advice and improve its knowledge base with the rule derived from this interaction. Interactive inductive learning based classification system is proposed for helping to compare university study courses semi-automatically.