Random Forests and Decision Trees
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.
Keywords: Random Forests, Decision Trees, J48.
Download Full-Text
ABOUT THE AUTHORS
Jehad Ali
Jehad Ali is pursuing his M.Sc Computer Systems Engineering from University of Engineering and Technology, Peshawar, Pakistan. He did his B.Sc. Computer Systems Engineering from the same university. He is working as a Computer Engineer in Ghulam Ishaq Khan Institute (GIKI) of Engineering Sciences and Technology, Topi, Pakistan. His research interest’s areas are image processing, computer vision, machine learning, Computer Networks and pattern recognition.
Rehanullah Khan
Rehanullah Khan graduated from the University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc (Information Systems) in 2006. He obtained PhD degree (Computer Engineering) in 2011 from Vienna University of Technology, Austria. He is currently an Associate Professor at the Sarhad University of Science and Technology, Peshawar. His research interests include color interpretation, segmentation and object recognition.
Nasir Ahmad
Nasir Ahmad graduated from University of Engineering and Technology Peshawar with a B.Sc Electrical Engineering degree. He obtained his PhD degree from UK in 2011. He is a faculty member of Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan. His Research Areas include Pattern Recognition, Computer vision and Digital Signal Processing.
Imran Maqsood
Imran Maqsood graduated from the University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc in 2006. He is pursuing his PhD degree. He is currently an Assistant Professor at the Department of Computer Software Engineering, UET Mardan Campus, Peshawar, Pakistan.
Jehad Ali
Jehad Ali is pursuing his M.Sc Computer Systems Engineering from University of Engineering and Technology, Peshawar, Pakistan. He did his B.Sc. Computer Systems Engineering from the same university. He is working as a Computer Engineer in Ghulam Ishaq Khan Institute (GIKI) of Engineering Sciences and Technology, Topi, Pakistan. His research interest’s areas are image processing, computer vision, machine learning, Computer Networks and pattern recognition.
Rehanullah Khan
Rehanullah Khan graduated from the University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc (Information Systems) in 2006. He obtained PhD degree (Computer Engineering) in 2011 from Vienna University of Technology, Austria. He is currently an Associate Professor at the Sarhad University of Science and Technology, Peshawar. His research interests include color interpretation, segmentation and object recognition.
Nasir Ahmad
Nasir Ahmad graduated from University of Engineering and Technology Peshawar with a B.Sc Electrical Engineering degree. He obtained his PhD degree from UK in 2011. He is a faculty member of Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan. His Research Areas include Pattern Recognition, Computer vision and Digital Signal Processing.
Imran Maqsood
Imran Maqsood graduated from the University of Engineering and Technology Peshawar, with a B.Sc degree (Computer Engineering) in 2004 and M.Sc in 2006. He is pursuing his PhD degree. He is currently an Assistant Professor at the Department of Computer Software Engineering, UET Mardan Campus, Peshawar, Pakistan.
