A NEW ACCURATE AND EFFICIENT APPROACH TO EXTRACT CLASSIFICATION RULES

A new method for extracting rules from multi-class datasets was proposed in this study. The proposed method was applied to 4 different data set. Discrete and real attributes were decoded in different ways. Discrete attributes were coded as binary whereas real attributes were coded by using two real values These values indicate the midpoint and the expansion of intervals of the attributes that form the rules. Classification success was used as fitness function of rule extraction. CLONALG which is Artificial Immune Systems (AIS) algorithm was used to optimize the fitness function. To apply the proposed method Iris, Wine, Glass and Abalone datasets were used. The datasets were obtained from machine learning repository of University of California at Irvine (UCI). The proposed method achieved prediction accuracy ratios of 100%, 99,44%, 77,10%, and 62,59% for Iris, Wine, Glass and Abalone datasets, respectively. When it is compared with the previous studies it has been seen that the proposed method achieved more successful results and has advantage in terms of complexity.