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