Title

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.

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

84973579573

Indexed in Scopus

yes

Open Access

no

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