Improved C4.5 algorithm for rule based classification

Abstract

C4.5 is one of the most popular algorithms for rule base classification. There are many empirical features in this algorithm such as continuous number categorization, missing value handling, etc. However in many cases it takes more processing time and provides less accuracy rate for correctly classified instances. On the other hand, a large dataset might contain hundreds of attributes. We need to choose most related attributes among them to perform higher accuracy using C4.5. It is also a difficult task to choose a proper algorithm to perform efficient and perfect classification. With our proposed method, we select the most relevant attributes from a dataset by reducing input space and simultaneously improve the performance of this algorithm. The improved performance is measured based on better accuracy and less computational complexity. We measure Entropy of Information Theory to identify the central attribute for a dataset. Then apply correlation coefficient measure namely, Pearson’s, Spearman, Kendall correlation utilizing the central attribute of the same dataset. We conduct a comparative study using these three most popular correlation coefficient measures to choose the best method on eight well known data mining problem from UCI (University of California Irvine) data repository. We use box plot to compare experimental results. Our proposed method shows better performance in most of the individual experiment.