Extracting classification rules from modified fuzzy min-max neural network for data with mixed attributes

Graphical abstractDisplay Omitted HighlightsProposed method modifies the fuzzy min-max neural network to handle both continuous and discrete attributes effectively.It prunes the trained network to reduce the number of hyperboxes created.Rules are extracted from the pruned network which justifies its classification results. This paper proposes the modified fuzzy min-max neural network (MFMMN) classification model to perform the supervised classification of data. The basic fuzzy min-max neural network (FMMN) can only be applied to the continuous attribute values and cannot handle the discrete values. Also justification of the classification results given by FMMN required to be obtained to make it more applicable to real world applications. These both issues are solved in the proposed MFMMN. In the MFMMN, each hyperbox have min-max values defined in terms of continuous attributes and a set of binary strings defined for discrete attributes. Bitwise 'and' and 'or' operators are used to update the discrete values associated with each hyperbox. The trained network is pruned to remove the less useful hyperboxes based on their confidence factor. The proposed model is applied to nine different datasets taken from the University of California, Irvine (UCI) machine learning repository. Finally the case study of a real time weather data is evaluated using MFMMN. The experimental results show that the proposed model has given very good accuracy. In addition to accuracy, the number of hyperboxes obtained after pruning are very less which lead to less number of concise rules and reduced computational complexity.