MC4.5 decision tree algorithm: an improved use of continuous attributes. (2nd April 2020) - Cherfi, Anis; Nouira, Kaouther; Ferchichi, Ahmed
C4.5 is one of the top ten data mining algorithms; it is the most widely used decision trees construction techniques. Although effective, it suffers from the problem of complexity when it deals with continuous attributes. It also leads to a certain level of information loss. Therefore, minimising such loss and reducing the time complexity is one of the main goals in this paper. With the intention of alleviating these problems, this paper presents a novel algorithm namely MC4.5, which proposes the statistical mean as an alternative to the C4.5 threshold selection process. To demonstrate the effectiveness of the new algorithm, a complete evaluation was launched to prove that MC4.5 complies with the objectives previously mentioned. From the theoretical perspective, we develop an analysis of the complexity to compare algorithms. Empirically, we conduct an experimental study using 30 datasets to prove that, in most cases, the proposed algorithm leads to smaller decision trees with better accuracy comparing to the C4.5 algorithm.