Inductive learning system learns classification from training examples and uses induced rules for classifying new instances. As the classification tasks are getting more complicated, a classifier may meet difficulties in class prediction. To improve predictive accuracy of the inductive learning classifier, collaborative approach between a machine and human expert would be useful. The proposed interactive inductive system in uncertain conditions can ask for human advice and improve its performance with a rule derived from this interaction. Interactive inductive learning based classification system is proposed to assist in a study course comparative analysis
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