Improvement accuracy for C4.5 decision tree algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 3، دوره 17، شماره 5، مرداد 2026، صفحه 25-42 اصل مقاله (807.42 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.33099.4925 | ||
| نویسندگان | ||
| Nima Rasekh؛ Daniyal Nasiri Bavil* | ||
| Department of Information Engineering, Padua University, Italy | ||
| چکیده | ||
| The decision tree is indeed the most widely used approach to represent classifiers. Initially, it has been studied in the field of decision theory and statistics. However, it was found to be effective in other disciplines, such as data mining, machine learning and pattern recognition. This research deals with the problem of finding the parameter settings of the decision tree algorithm in order to achieve higher accuracy for a given domain. The proposed approach, Improved C4.5 (IC4.5), is a supervised learning model based on the C4.5 algorithm to construct a decision tree. The modification to the C4.5 algorithm includes using improved gain instead of the gain ratio measure to choose the best attribute and increase the accuracy of the decision tree. The introduced algorithm has been experimented with on some data sets from the UCI repository. The results obtained from experiments show that the accuracy of IC4.5 is greater than C4.5 in increasing the accuracy of the decision tree. | ||
| کلیدواژهها | ||
| decision tree؛ data mining؛ C4.5 algorithm؛ IC4.5؛ accuracy؛ classifiers | ||
| مراجع | ||
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