期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：5
出版社：IJCSI Press
摘要：In this paper, we have compared the classification results of two models i.e. Random Forest and the J48 for classifying twenty versatile datasets. We took 20 data sets available from UCI repository [1] containing instances varying from 148 to 20000. We compared the classification results obtained from methods i.e. Random Forest and Decision Tree (J48). The classification parameters consist of correctly classified instances, incorrectly classified instances, F-Measure, Precision, Accuracy and Recall. We discussed the pros and cons of using these models for large and small data sets. The classification results show that Random Forest gives better results for the same number of attributes and large data sets i.e. with greater number of instances, while J48 is handy with small data sets (less number of instances). The results from breast cancer data set depicts that when the number of instances increased from 286 to 699, the percentage of correctly classified instances increased from 69.23% to 96.13% for Random Forest i.e. for dataset with same number of attributes but having more instances, the Random Forest accuracy increased.
