Integrated Associative Classification and Neural Network Model Enhanced by Using a Statistical Approach

Abstract

Association rules is a novel data mining techniquethat has been mainly used for data description,exploration and prediction in knowledge discovery and decision support systems.The association rulemining algorithm is modified to handle the user-defined input constraints. Associative classificationisprovided with a large number of rules, from which aset of quality rules are chosen to develop an efficientclassifier. Many attribute selection measures are used to reduce the number of generated rules. In thispaper the pruning of rule sets is facilitated by chi squared analysis thereby only positively correlated rulesare used in the classifier. Also the Neural NetworkAssociative Classification system is used in ordertoimprove the accuracy of the classifier. The trainednetwork is then used to classify the future data.Theperformance of the Neural Network Associative Classification system is analyzed with the datasets fromUCI machine learning repository

Boundness of a Neural Network Weights Using the Notion of a Limit of a Sequence

“…The gradient descent technique is among the common techniques that are used to perform the search process that was mentioned by Mitchell (1997) by optimizing the weights of a neural classifier, which is achieved by applying the delta rule that is used to find out the amount by which the current value of a weight will be updated. Mathew (2013) believes that "The most popular neural network algorithm is backpr opagation, a kind of gradient descent method. Backpropagation iteratively process the data set, comparing the network's prediction for each tuple with actual known target value to find out an acceptable local minimum in the NN weight space in turns achieves the least number of errors".…”