CRNN: Integrating classification rules into neural network

Association classification has been an important type of the rule-based classification. A variety of approaches have been proposed to build a classifier based on classification rules. In the prediction stage of the extant approaches, most of the existing association classifiers use the ensemble quality measurement of each rule in a subset of rules to predict the class label of the new data. This method still suffers the following two problems. The classification rules are used individually thus the coupling relations between rules [1] are ignored in the prediction. However, in real-world rule set, rules are often inter-related and a new data object may partially satisfy many rules. Furthermore, the classification rule based prediction model lacks a general expression of the decision methodology. This paper proposes a classification method that integrating classification rules into neural network (CRNN, for short), which presents a general form of the rule based decision methodology by rule-based network. In comparison with the extant rule-based classifiers, such as C4.5, CBA, CMAR and CPAR, our approach has two advantages. First, CRNN takes the coupling relations between rules from the training data into account in the prediction step. Second, CRNN automatically obtains higher performance on the structure and parameter learning than traditional neural network. CRNN uses the linear computing algorithm in neural network instead of the costly iterative learning algorithm. Two ways of the classification rule set generation are conducted in this paper for the CRNN evaluation, and CRNN achieves the satisfactory performance.