Accepted Papers

  • ATTRIBUTE REDUCTION-BASED ENSEMBLE RULE CLASSIFIERS METHOD FOR DATASET CLASSIFICATION
    Mohammad Aizat bin Basir1 and Faudziah binti Ahmad2 1Universiti Malaysia Terengganu (UMT)Terengganu, 2Malaysia Universiti Utara Malaysia(UUM) Kedah, Malaysia
    ABSTRACT

    Attribute reduction and classification task are an essential process in dealing with large data sets that comprise numerous number of input attributes. There are many search methods and classifiers that have been used to find the optimal number of attributes. The aim of this paper is to find the optimal set of attributes and improve the classification accuracy by adopting ensemble rule classifiers method. Research process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for classification task. Results are in terms of percentage of accuracy and number of selected attributes and rules generated. 6 datasets were used for the experiment. The final output is an optimal set of attributes with ensemble rule classifiers method. The experimental results conducted on public real dataset demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy on the selected dataset. Significant improvement in accuracy and optimal set of attribute selected is achieved by adopting ensemble rule classifiers method.