Comparing different stopping criteria for fuzzy decision tree induction through IDFID۳
Publish place: Iranian Journal of Fuzzy Systems، Vol: 11، Issue: 1
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
View: 200
This Paper With 22 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_IJFS-11-1_003
تاریخ نمایه سازی: 31 خرداد 1401
Abstract:
Fuzzy Decision Tree (FDT) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. When a FDT induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. Finding a proper threshold value for a stopping criterion is one of the greatest challenges to be faced in FDT induction. In this paper, we propose a new method named Iterative Deepening Fuzzy ID۳ (IDFID۳) for FDT induction that has the ability of controlling the tree’s growth via dynamically setting the threshold value of stopping criterion in an iterative procedure. The final FDT induced by IDFID۳ and the one obtained by common FID۳ are the same when the numbers of nodes of induced FDTs are equal, but our main intention for introducing IDFID۳ is the comparison of different stopping criteria through this algorithm. Therefore, a new stopping criterion named Normalized Maximum fuzzy information Gain multiplied by Number of Instances (NMGNI) is proposed and IDFID۳ is used for comparing it against the other stopping criteria. Generally speaking, this paper presents a method to compare different stopping criteria independent of their threshold values utilizing IDFID۳. The comparison results show that FDTs induced by the proposed stopping criterion in most situations are superior to the others and number of instances stopping criterion performs better than fuzzy information gain stopping criterion in terms of complexity (i.e. number of nodes) and classification accuracy. Also, both tree depth and fuzzy information gain stopping criteria, outperform fuzzy entropy, accuracy and number of instances in terms of mean depth of generated FDTs.
Keywords:
Authors
Mohsen Zeinalkhani
Department of Computer Engineering, Shahid Bahonar Uni- versity of Kerman, Kerman, Iran
Mahdi Eftekhari
Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, 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 :