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A comparative study of pruned decision trees and fuzzy decision trees
مقدم أطروحة جامعية
مشرف أطروحة جامعية
الجامعة
جامعة الأخوين
الكلية
كلية الهندسة و العلوم
القسم الأكاديمي
علوم الحاسب
دولة الجامعة
المغرب
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2000
الملخص الإنجليزي
Decision trees are a widely used symbolic technique for classification tasks in machine learning and they successfully compete with other models such as neural networks.
However, they have often been criticized for some of their limitations such as problems of handling continuous attributes, missing data and performance in noisy domains.
Some of these limitations have been addressed by some models such as CART and C4.5.
Yet, the resulting knowledge exhibits lower comprehensibility and over speciahzation.
These problems were in turn addressed by tree pruning techniques and, more recently, by combining fuzzy representation with decision trees.
We propose in this thesis, a comparative study of pruned decision trees and fuzzy decision trees.
The focus is twofold.
First, for crisp decision tree experiments, we use C4.5 and apply and compare five post-pruning methods to perform pruning in C4.5 [6], Second, FID3.0 is used to build fuzzy decision trees.
The fuzzy decision tree algorithm requires data with predefined fuzzy sets for the continuous-valued attributes.
We have experimented with (1) the inductive reasoning algorithm, and (2) a fuzzy clustering algorithm (FCM) to construct those fuzzy sets.
Furthermore, we have compared three methods (random selection, heuristic-based selection and cluster validity-based selection) for selecting granularity, or the number of fuzzy sets to use for each continuous-valued input variable.
Numerical experiments are performed on 12 data sets to compare the performance of the different trees.
Among the five pruning methods, “Error Based Pruning” algorithm showed better performance stability over 12 data sets.
For fuzzy decision tree, using cluster validity produces the best results for deciding on the granularity of the input fuzzy sets.
Combining this choice of granularity with the use of FCM to build fuzzy membership functions produces superior results.
Cross validation experiments and statistical significance tests reveal that this combination produces fuzzy decision trees that are superior to those of the “Error Based Pruning” algorithm in 6 out of 12 data sets, are inferior for one data set and are equivalent for the remaining 5 data sets.
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
عدد الصفحات
69
قائمة المحتويات
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Crisp pruned decision trees
Chapter Three : Fuzzy decision trees.
Chapter Four : Experiments and results.
Chapter Five : Conclusion.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bin Ibrahim, Huda. (2000). A comparative study of pruned decision trees and fuzzy decision trees. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-629787
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bin Ibrahim, Huda. A comparative study of pruned decision trees and fuzzy decision trees. (Master's theses Theses and Dissertations Master). Al Akhawayn University. (2000).
https://search.emarefa.net/detail/BIM-629787
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bin Ibrahim, Huda. (2000). A comparative study of pruned decision trees and fuzzy decision trees. (Master's theses Theses and Dissertations Master). Al Akhawayn University, Morocco
https://search.emarefa.net/detail/BIM-629787
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-629787
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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