An Experimental Study on Decision Tree Classifier Using Discrete and Continuous Data
Classification is one of the fundamental tasks of pattern recognition, data mining, and big data analysis. It spans across the domain for classifying novel instances whose class labels are unknown prior to the development of model. Decision trees like ID3, C4.5, and other variants for the task of classification have been widely studied in pattern recognition and data mining. The reason is that decision tree classifier is simple to understand, and its performance has been comparable with many promising classifiers. Therefore, in this work, we have developed a two-phase method of decision tree classifier for classifying continuous and discrete data effectively. In phase one, our method examines the database, whether it is a continuous-valued or discrete-valued database. If it is a continuous-valued database, then the database is discretized in this phase. In the second phase, the classifier is built and then classifies an unknown instance. To measure the performance of these two phases, we have experimented on a few datasets from the University of California, Irvine (UCI) Machine Learning repository and one artificially created dataset. The experimental evidence shows that this two-phase method of constructing a decision tree to classify an unknown instance is effective in both continuous and discrete cases.
KeywordsDecision tree Classification Discretization Data mining
Thanks to Mr. Sagar Muduli, MCA student, Dept. of I & CT, F. M. University, Balasore, Odisha, for his notable contribution in this work.
- 1.Phyu, TN.: Survey of classification techniques in data mining. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. 1, pp. 18–20 (2009)Google Scholar
- 7.Breiman, L.: Classification and regression trees. Routledge (2017)Google Scholar
- 8.Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier (2011)Google Scholar
- 9.Jearanaitanakij, K.: Classifying continuous data set by id3 algorithm. In: Information, Communications and Signal Processing, 2005 Fifth International Conference, pp. 1048–1051. IEEE (2005)Google Scholar
- 13.Uther, W.T., Veloso, M.M.: Tree based discretization for continuous state space reinforcement learning. In: Aaai/iaai, pp. 769–774 (1998)Google Scholar
- 15.Dheeru, D., Taniskidou, E.K.: UCI machine learning repository (2017)Google Scholar