A meta-heuristic approach for developing PROAFTN with decision tree
Document Type
Conference Proceeding
Source of Publication
2016 3rd MEC International Conference on Big Data and Smart City, ICBDSC 2016
Publication Date
4-26-2016
Abstract
© 2016 IEEE. Machine learning algorithms known for their performance in using historical data and examples to predict and classify unknown instances. Decision tree is an efficient machine learning approach that can use data only without the involvement of decision maker to improve the decision making process. Multi-Criteria Decision Analysis (MCDA)is another paradigm used for data classification. In this paper, we propose a new fuzzy classification method based on MCDA called PROAFTN. To use PROAFTN, a set of parameters need to be established from data. The proposed approach uses data pre-processing and canonical genetic algorithm (GA) for obtaining these parameters from data. The generated models have been applied on popular data selected from several application domain, health, economy, etc. According to our experimental study, the new model performs significantly better than decision trees according in terms of accuracy and the interpretation of the decision rules.
DOI Link
ISBN
9781509013654
Publisher
Institute of Electrical and Electronics Engineers Inc.
First Page
211
Last Page
217
Disciplines
Computer Sciences
Keywords
Decision Tree, MCDA, PROAFTN
Scopus ID
Recommended Citation
Al-Obeidat, Feras; Shah, Babar; Khattak, Asad Masood; and Abbas, Ali, "A meta-heuristic approach for developing PROAFTN with decision tree" (2016). All Works. 161.
https://zuscholars.zu.ac.ae/works/161
Indexed in Scopus
yes
Open Access
no