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 | Journal of Computer Science and Information Technology December 2018, Vol. 6, No. 2, pp. 23-29 ISSN: 2334-2366 (Print), 2334-2374 (Online) Copyright © The Author(s). All Rights Reserved. Published by American Research Institute for Policy Development DOI: URL: The Quadratic Entropy Approach to Implement the Id3 Decision Tree Algorithm Adewole Adetunji Philip1 & Udeh Stanley Nnamdi2 Abstract 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. Keywords: Decision tree, ID3, Entropy, Quadratic entropy, Data mining, Classification Introduction Data mining, according to [1], is the extraction of hidden predictive information and unknown data, patterns, relationships and knowledge by exploring the large data sets which are difficult to find and detect with traditional statistical methods. Data mining is also known as analyzing data to discover a pattern and using that pattern to make a prediction for future occurrence of same or similar situation. The major tasks in .
