A Survey of Classification Methods Utilizing Decision Trees

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2015 by IJETT Journal
Volume-22 Number-4
Year of Publication : 2015
Authors : Prerna Kapoor, Reena Rani
DOI :  10.14445/22315381/IJETT-V22P240


Prerna Kapoor, Reena Rani"A Survey of Classification Methods Utilizing Decision Trees", International Journal of Engineering Trends and Technology (IJETT), V22(4),188-194 April 2015. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Therecognition of outlines and the invention of decision rules from data is one of the challenging setbacks in discovering and learning.When continuous attributes are involved in the process the attributes should be discretized with threshold values or with various other standardizing methods. Decision tree induction algorithms craft decision trees by recursively partitioning the input space. Hence, a rule tree is obtained by traversal from the origin node to every single leaf node in the tree. The decision trees can be fiercely embodied as a set of decision laws (if-then-else rules) to assist the understanding. Inductive discovering methods craft such decision trees, frequently established on heuristicdata or statistical probability concerning attributes. This paper is about Decision Trees algorithms and their implementation mainly C4.5.


[1]. Rodrigo Coelho Barros,,Marcio Porto Basgalupp, A. C. P. L. F. De Carvalho, and Alex AlvesFreitas. "A survey of evolutionary algorithms for decision-tree induction." Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 42, no. 3 (2012): 291-312.
[2]. W. Nor Haizan W. Mohamed,,MohdNajibMohdSalleh, and Abdul Halim Omar. "A comparative study of reduced error pruning method in decision tree algorithms." In Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on, pp. 392-397. IEEE, 2012.
[3]. Tina R. Patil, and M. S. Sherekar. "Performance analysis of Naive Bayes and J48 classification algorithm for data classification." Int J ComputSciAppl 6 (2013): 256-261.
[4]. Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, and Sau Dan Lee. "Decision trees for uncertain data." Knowledge and Data Engineering, IEEE Transactions on 23, no. 1 (2011): 64-78.
[5]. Rodrigo C. Barros, Márcio P. Basgalupp, André CPLF de Carvalho, and Alex A. Freitas. "Towards the automatic design of decision tree induction algorithms." In Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, pp. 567-574. ACM, 2011.
[6]. Raj Kumar and Rajesh Verma. "Classification algorithms for data mining: A survey." International Journal of Innovations in Engineering and Technology (IJIET) 1, no. 2 (2012): 7-14.
[7]. A.S. Galathiya, A. P. Ganatra, and C. K. Bhensdadia. "Improved Decision Tree Induction Algorithm with Feature Selection, Cross Validation, Model Complexity and Reduced Error Pruning." International Journal of Computer Science and Information Technologies 3, no. 2 (2012): 3427-3431.
[8]. Susan Lomax and Sunil Vadera. "A survey of cost-sensitive decision tree induction algorithms." ACM Computing Surveys (CSUR) 45, no. 2 (2013): 16.
[9]. Mohammed Abdul Khaleel, Sateesh Kumar Pradham, and G. N. Dash. "A survey of data mining techniques on medical data for finding locally frequent diseases." Int. J. Adv. Res. Comput. Sci. Softw. Eng 3, no. 8 (2013).
[10]. AnujaPriyama, Rahul GuptaaAbhijeeta, AnjuRatheeb, and SaurabhSrivastavab. "Comparative Analysis of Decision Tree Classification Algorithms." International Journal of Current Engineering and Technology 3, no. 2 (2013): 866-883.
[11]. Richa Sharma, AniruddhaGhosh, and P. K. Joshi. "Decision tree approach for classification of remotely sensed satellite data using open source support." Journal of Field System Science 122, no. 5 (2013): 1237-1247.
[12]. LeszekRutkowski, Lena Pietruczuk, PiotrDuda, and MaciejJaworski. "Decision trees for mining data streams based on the McDiarmid`s bound." Knowledge and Data Engineering, IEEE Transactions on 25, no. 6 (2013): 1272-1279.
[13]. Nirmal Kumar, G. P. Reddy, and S. Chatterji. "Evaluation of Best First Decision Tree on Categorical Soil Survey Data for Land Capability Classification." International Journal of Computer Applications 72, no. 4 (2013).
[14]. DursunDelen, CemilKuzey, and Ali Uyar. "Measuring firm performance using financial ratios: A decision tree approach." Expert Systems with Applications 40, no. 10 (2013): 3970-3983.
[15]. KalpeshAdhatrao, AdityaGaykar, AmirajDhawan, RohitJha, and VipulHonrao. "Predicting Students` Performance using ID3 and C4. 5 Classification Algorithms." arXiv preprint arXiv:1310.2071 (2013).
[16]. DelveenLuqmanAbd, AL-Nabi, , and ShereenShukri Ahmed. "Survey on Classification Algorithms for Data Mining:(Comparison and Evaluation)." Computer Engineering and Intelligent Systems 4, no. 8 (2013): 18-24.
[17]. Michal Wozniak, Manuel Graña, and Emilio Corchado. "A survey of multiple classifier systems as hybrid systems." Information Fusion 16 (2014): 3-17.
[18]. Brijain R. Patel, and Kaushik K. Rana. "Use of Renyi Entropy Calculation Method for ID3 Algorithm for Decision tree Generation in Data Mining." International Journal 2, no. 5 (2014).
[19]. Katz, Gilad, AsafShabtai, LiorRokach, and NirOfek. "ConfDTree: A Statistical Method for Improving Decision Trees." Journal of Computer Science and Technology 29, no. 3 (2014): 392-407.
[20]. M.Rajyalakshmi, P.Srinivasulu.”High Speed Improved Decision Tree for Mining Streaming Data.”International Journal of Engineering Trends and Technology (IJETT) – Volume 18 Number 8 – Dec 2014.

Machine Learning, Data mining, Decision trees, C4.5, J48.