Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest

To analyze the raw data manually and find the correct information from it is a tough process. But Data mining technique automatically detect the relevant patterns or information from the raw data, using the data mining algorithms. In Data mining algorithms, Decision trees are the best and commonly used approach for representing the data. Using these Decision trees, data can be represented as a most visualizing form. Many different decision tree algorithms are used for the data mining technique. Each algorithm gives a unique decision tree from the input data. This paper focus on the comparison of different decision tree algorithms for data analysis.

[1]  Chen Jin,et al.  An improved ID3 decision tree algorithm , 2009, 2009 4th International Conference on Computer Science & Education.

[2]  Vili Podgorelec,et al.  Decision Trees: An Overview and Their Use in Medicine , 2002, Journal of Medical Systems.

[3]  Haoran Guo,et al.  Performance Analysis between Different Decision Trees for Uncertain Data , 2012, 2012 International Conference on Computer Science and Service System.

[4]  Wang Miao,et al.  Improved classification attribute selection scheme for decision tree , 2010 .

[5]  Steven Salzberg,et al.  Programs for Machine Learning , 2004 .

[6]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[7]  Robert Givan,et al.  Online Ensemble Learning: An Empirical Study , 2000, Machine Learning.

[8]  Salvatore Ruggieri,et al.  Efficient C4.5 , 2002, IEEE Trans. Knowl. Data Eng..

[9]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[10]  Chun Guan,et al.  An Improved ID3 Based on Weighted Modified Information Gain , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.

[11]  Arjen P. de Vries,et al.  Relevance information: a loss of entropy but a gain for IDF? , 2005, SIGIR '05.

[12]  Sotiris B. Kotsiantis.  A hybrid decision tree classifier , 2014, J. Intell. Fuzzy Syst..

[13]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[14]  Joaquín Abellán,et al.  Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring , 2014, Expert Syst. Appl..

[15]  David L. Olson,et al.  Comparative analysis of data mining methods for bankruptcy prediction , 2012, Decis. Support Syst..

[16]  Thomas Plum,et al.  Efficient C , 1985 .

[17]  Peter A. Flach,et al.  Rule induction , 2003 .

[18]  P. Kokol,et al.  End user friendly data mining with decision trees: a reality or a wish? , 2007 .