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Decision trees have been a useful tool in data mining for building useful intelligence in diverse areas of research to solve real world problems of data classifications. One decision tree algorithm that has been predominant for its robust use and wide acceptance has been the Iterative Dichotomiser 3 (ID3). The splitting criteria for the algorithm have been the Shannon algorithm for evaluating the entropy of the dataset. In this research work, the implementation of the ID3 algorithm using the Quadratic entropy algorithm in a bid to improve the accuracy of classification of the ID3 algorithm was carried out. The results show that the implementation of the ID3 algorithm using the quadratic entropy with some selected datasets have a significant improvement in the areas of its accuracy as compared with the traditional ID3 implementation using the Shannon entropy. The formulated model makes use of similar process of the ID3 algorithm but replaces the Shannon entropy formula with the Quadratic entropy.

The Quadratic Entropy Approach to Implement the Id3 Decision Tree Algorithm