Open Access Pub publishes peer-reviewed, free-to-read open-access articles. Showing
articles matching C4.5 algorithm — open any to read the full text,
or download the PDF or XML.
Y.FakirCorresponding author Laboratory of Information Processing and Decision Support, Faculty of Sciences and Technics, Sultane Moulay Slimane University, Beni Mellal, Morocco
Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model