این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Industrial Engineering and Management Studies، جلد ۹، شماره ۲، صفحات ۸۶-۱۱۲
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Machine learning decision tree based on regression in data mining to extract more knowledge
چکیده انگلیسی مقاله In a data-driven decision-making process, there are various types of data that should be thoroughly processed and analyzed. Data mining is a well-recognized method to obtain such information by analyzing data and transforming it into actionable insights for further use. Among the various data mining techniques such as classification, clustering, and association rules, this research focused on classification techniques and presented an innovative regression-based learning approach in the decision tree (DT) models. DT algorithms are easy-to-understood and can work with different data types including continuous, discrete, and non-numerical. Despite a large number of existing studies, which attempt to enhance the performance of the DT models, there is still a gap in accurately extracting knowledge from databases. In this research, this issue is addressed by exploiting regression and coefficient of determination (R2) methods in a DT. The proposed tree provides new insights in the following aspects: split criterion, handling continuous and discrete variables, labeling leaf node, pruning process by stopping criteria and tree evaluation. The superiority of the proposed algorithm is demonstrated using a real-world hospital database and a comparison with existing approaches is provided. The results showed that the proposed algorithm outperforms the existing methods in terms of higher accuracy and lower complexity.
کلیدواژه‌های انگلیسی مقاله Data mining,Classification,decision tree,split criterion,R square
نویسندگان مقاله Zahra Jiryaei Sharahi |
Department of Industrial Engineering, College of Engineering, University of Yazd, Yazd, Iran.

Yahia Zare Mehrjerdi |
Department of Industrial Engineering, College of Engineering, University of Yazd, Yazd, Iran.

Mohammad Saleh Owlia |
1 Department of Industrial Engineering, College of Engineering, University of Yazd, Yazd, Iran.

Masoud Abessi |
Department of Industrial Engineering, College of Engineering, University of Yazd, Yazd, Iran.

نشانی اینترنتی https://jiems.icms.ac.ir/article_166211_d7bfd6441e0c3f4764d27da30fd61637.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات