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Mounir Dhibi
PMI 09/UR/13-0, Zarroug ISSAT Gafsa 2112 Tunisia
Computer Science & IT Electrical & Electronics Engineering

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Credal Fusion of Classifications for Noisy and Uncertain Data Fatma Karem; Mounir Dhibi; Arnaud Martin; Med Salim Bouhlel
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1424.368 KB) | DOI: 10.11591/ijece.v7i2.pp1071-1087

Abstract

This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from  data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems. We materialize our main idea by using belief function theory to do combination between classification and clustering. This theory treats very well imprecision and uncertainty linked to classification. Experimental results show that our approach has ability to significantly improve the quality of classification of generic database.
Co-Authors Arnaud Martin Fatma Karem Med Salim Bouhlel