The Construction of Scalable Decision Tree based on Fast Splitting and J-Max Pre-Pruning on Large Datasets
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 280
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJE-34-8_001
تاریخ نمایه سازی: 12 مرداد 1400
Abstract:
The decision tree is one of the most important algorithms in the classification which offers a comprehensible model of data. In building a tree we may encounter a memory limitation. The present study aimed to implement an incremental scalable approach based on fast splitting and present pruning to construct the decision tree on a large dataset as the complexity of the tree decreases. The proposed algorithm constructs the decision tree without storing the entire dataset in the primary memory by using a minimum number of parameters. Furthermore, the J-max Pre pruning method was used to reduce the complexity with acceptable results. Experimental results show that this approach can create a balance between the accuracy and complexity of the tree and overcome the difficulties of the complexity of the tree. In spite of the appropriate accuracy and time, the proposed algorithm could produce a decision tree with less complexity on the large dataset.
Keywords:
Authors
S. Lotfi
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
M. Ghasemzadeh
Computer Department, Engineering Campus, Yazd University.
M. Mohsenzadeh
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
M. Mirzarezaee
Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :