Conflict-sensitivity contexture learning algorithm for mining interesting patterns using neuro-fuzzy network with decision rules

Most real-world data analyzed by classification techniques is imbalanced in terms of the proportion of examples available for each data class. This class imbalance problem would impede the performance of some standard classifiers since a modal-class pattern may cover many relatively weak interest patterns. This study presents a new learning algorithm based on conflict-sensitive contexture, which remedies the class imbalance problem by basing decisions on the inconsistency of the local entropy estimator. The study also adopts a new neuro-fuzzy network algorithm with multiple decision rules to a real-world banking case for mining very significant patterns. The proposed algorithm can attain accuracy for minority classes at classification from roughly 10% up to 71%. This work also elucidates these patterns of interests and suggests many business applications for them.