A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems

Generally, many classifier systems compel in the classification of multi-class problems. The aim of this study is to improve the classification accuracy in the case of multi-class classification problems. In this study, we have proposed a novel hybrid classification system based on C4.5 decision tree classifier and one-against-all approach to classify the multi-class problems including dermatology, image segmentation, and lymphography datasets taken from UCI (University of California Irvine) machine learning database. To test the proposed method, we have used the classification accuracy, sensitivity-specificity analysis, and 10-fold cross validation. In this work, firstly C4.5 decision tree has been run for all the classes of dataset used and achieved 84.48%, 88.79%, and 80.11% classification accuracies for dermatology, image segmentation, and lymphography datasets using 10-fold cross validation, respectively. The proposed method based on C4.5 decision tree classifier and one-against-all approach obtained 96.71%, 95.18%, and 87.95% for above datasets, respectively. These results show that the proposed method has produced very promising results in the classification of multi-class problems. This method can be used in many pattern recognition applications. In future, instead of C4.5 decision tree, other classification algorithms such as Bayesian learning, artificial immune system algorithms, artificial neural networks can be used.