Development of multi-label classification algorithm based on correlations among labels
مقدم أطروحة جامعية
مشرف أطروحة جامعية
Thabtah, Fadi Fayiz Abd al-Jabir
الجامعة
جامعة فيلادلفيا
الكلية
كلية تكنولوجيا المعلومات
القسم الأكاديمي
قسم علم الحاسوب
دولة الجامعة
الأردن
الدرجة العلمية
ماجستير
تاريخ الدرجة العلمية
2013
الملخص الإنجليزي
-Multi label classification is concerned with learning from set of instances that are associated with a set of labels, that is, an instance could be associated with multiple labels at the same time.
This task occurs frequently in application areas like text categorization, multimedia classification, bioinformatics, protein function classification and semantic scene classification.
Current multi-label classification methods could be divided into two parts.
The first part is called problem transformation methods, which transform multi-label classification problem into single label classification problem, and then apply any single label classifier to solve the problem.
The second part is called algorithm adaptation methods, which adapt an existing single label classification algorithm to handle multi-label data.
The following are some of the research challenges in the field of multi-label classification problem: 1.
Design a hierarchical structure for multi- label to manage label correlationships.
2.
To extract relevant label sets from multi-label data set.
3.
A novel approach that uses both problem transformation methods, and algorithm adaptation methods, to improve performance and accuracy for multi-label classifier.
In this thesis, we propose a multi-label classification algorithm based on correlations among labels, that uses both problem transformation methods and algorithm adaptation method.
The algorithm begins with transforming multi-label dataset into single label dataset using least frequent label criteria, and then applies PART algorithm on the transformed dataset.
Also the algorithm tries to get benefit from positive correlations among labels using predictive Apriori algorithm.
The output of the algorithm is multilabels rules.
The algorithm has been evaluated using two multi-label datasets ( "Emotions"," Yeast") and three evaluation measures (Accuracy, Hamming Loss, Harmonic Mean).
Further, we show by experiments that this algorithm has a fair accuracy comparing with other related algorithms.
التخصصات الرئيسية
الموضوعات
عدد الصفحات
61
قائمة المحتويات
Table of contents.
Abstract.
Chapter one : Introduction.
Chapter Two : Overview of multi-label classification methods.
Chapter Three : The proposed model : development of multi-label classification algorithm based on labels correlations.
Chapter Four : Data and experiments.
Chapter Five : Conclusions and future work.
References.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Diyab, Raid Hasan Salih. (2013). Development of multi-label classification algorithm based on correlations among labels. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-546361
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Diyab, Raid Hasan Salih. Development of multi-label classification algorithm based on correlations among labels. (Master's theses Theses and Dissertations Master). Philadelphia University. (2013).
https://search.emarefa.net/detail/BIM-546361
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Diyab, Raid Hasan Salih. (2013). Development of multi-label classification algorithm based on correlations among labels. (Master's theses Theses and Dissertations Master). Philadelphia University, Jordan
https://search.emarefa.net/detail/BIM-546361
لغة النص
الإنجليزية
نوع البيانات
رسائل جامعية
رقم السجل
BIM-546361
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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