Third International conference on advances in communication, network and computing

Research Article

Construct Fuzzy Decision Trees Based on Roughness Measures

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  • @INPROCEEDINGS{10.1007/978-3-642-35615-5_29,
        author={Mohamed Elashiri and Hesham Hefny and Ashraf Abd Elwhab},
        title={Construct Fuzzy Decision Trees Based on Roughness Measures},
        proceedings={Third International conference on advances in communication, network and computing},
        proceedings_a={CNC},
        year={2012},
        month={12},
        keywords={Fuzzy set Rough set Fuzzy decision tree Accuracy measure Roughness measure},
        doi={10.1007/978-3-642-35615-5_29}
    }
    
  • Mohamed Elashiri
    Hesham Hefny
    Ashraf Abd Elwhab
    Year: 2012
    Construct Fuzzy Decision Trees Based on Roughness Measures
    CNC
    Springer
    DOI: 10.1007/978-3-642-35615-5_29
Mohamed Elashiri1,*, Hesham Hefny2,*, Ashraf Abd Elwhab3,*
  • 1: Academy of Specialized Studies
  • 2: Cairo University
  • 3: Electronics Research Institute
*Contact email: ashiry@aun.edu.eg, hehefny@ieee.org, awahab@mcit.gov.eg

Abstract

Data mining is a process of extracting useful patterns and regularities from large bodies of data. Decision trees (DT) is one of data mining techniques used to deal with classical data. Fuzzy Decision Trees (FDT) is generalization of crisp decision trees, which aims to combine symbolic decision trees with approximate reasoning offered by fuzzy representation. Given a fuzzy information system (FIS), fuzzy expanded attributes play a crucial role in fuzzy decision trees. In this paper the problem is slowness and complexities of the fuzzy decision trees, but its rules are more accurate. Our target is to simplify computational procedures and increase the accuracy rules or to keep the high grade of accuracy and to select an efficient criterion to select fuzzy expanded attributes based on rough set theory.