Comparison of Decision Tree and ANN Techniques for Data Classification

As the development of the welfare is concerned, many factors had to be constantly monitored to achieve reliable solutions in various fields particularly in the education domain. The first and foremost goal of any educational system is to continually maintain and increase the graduation rates periodically. To make this work better the performance and attitude of the pupil towards the education should be carefully monitored. Generally, performance analysis and monitoring involves gathering both formal and informal data to help decision making process of any domain to achieve their goals. It eliminates several perspectives on a problem and proposes a solution based on the data what is discovered. The core and primary function of performance analysis model is classification. Various techniques of classification are used to improve the accuracy and reliability of prediction. This paper compares decision tree algorithms J4.8 and ID3 with Cascade-Correlation (CC) algorithm of ANN. The Cascade-Correlation algorithms has several advantages that it learns the population very quickly, and also the network and topology determines its own size and it retains the structures even when the training set changes. This methodology extracts highly useful, reliable and novel patterns from the dataset and the patterns obtained are compared by means of decision tree algorithms J4.8, ID3 with ANN for result prediction to resolve the problem domain. Keywords—ANN, CC, Data Mining, ID3, J4.8, Text