An Improved C4.5 Model Classification Algorithm Based on Taylor's Series

C4.5 is one of the most popular algorithms for rule base classification. Many empirical features in the algorithm exist, such as continuous number categorization, missing value handling and over-fitting. However, despite its promising advantage over the Iterative Dichotomiser 3 (ID3), C4.5 has the major setback of presenting the equivalent result as the ID3, especially when the same number of attributes is used. This paper proposes a technique that will handle the setback reported in C4.5. The performance of the proposed technique is measured based on better accuracy. The Entropy of Information Theory is measured to identify the central attribute for the dataset. The researchers apply exponential splitting information (EC4.5) in utilizing the central attribute of the same dataset. The result obtained on introducing Taylor series suggested a far better result than when the C4.5 (gain ratio) was introduced.