TY - JOUR AU - Yazirli, Yonca AU - Kan-Kilinç, Betül PY - 2019/08/31 Y2 - 2024/03/28 TI - COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION JF - International Journal of Research -GRANTHAALAYAH JA - Int. J. Res. Granthaalayah VL - 7 IS - 8 SE - Articles DO - 10.29121/granthaalayah.v7.i8.2019.689 UR - https://www.granthaalayahpublication.org/journals/granthaalayah/article/view/IJRG19_A08_2620 SP - 394-401 AB - <p>There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.</p> ER -