Micro-Cluster Based Semi-Supervised Data Stream Classification

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Micro-Cluster Based Semi-Supervised Data Stream Classification

ارائه دهنده: Provider: Meysam Masoumi

اساتید راهنما: Supervisors: Dr.Morteza Yousef Sanati

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. Muharram Mansoorizadeh - Dr. Mehdi Sakhaei-nia

زمان و تاریخ ارائه: Time and date of presentation: 2022-1-12 14:00

مکان ارائه: Place of presentation: http://vc.basu.ac.ir/eng-thesis04/

چکیده: Abstract: Nowadays large amounts of data are generated rapidly and seamlessly in data stream. The input stream category is among the practical methods of handling data stream; therefore, many methods have been proposed in recent years for data stream classification. Categorized as various classes, the existing methods can be analyzed from different perspectives. The first perspective is to analyze and categorize the existing methods based on the learning algorithm type. In the supervised data stream classification methods, true labels of every input sample must be available after classification in order to update the algorithm. However, it is difficult and time-consuming to access the true labels of data in the real world. Hence, the semi-supervised learning methods yield better outputs in the real-world applications. In another perspective, the existing methods are categorized with respect to the baseline classifier method. In fact, a simple yet efficient method of data stream classification is to use the KNN classifier and the sub-cluster-based methods for creating the decision-making boundary along with data summarization. At the same time, the major real-world challenge is to provide sufficient accuracy in addition to maintaining appropriate speed and execution memory. The proposed method reduced the time complexity of the base method from \mathbit{O}({\mathbit{maxMC}}^\mathbf{2}) to \mathbit{O}(\mathbit{maxMClog}(\mathbit{maxMC})) by applying the block-based approach and using the density-based criteria and the error-driven-based approach. The results of testing real-world datasets indicated 47% of improvement in the runtime of the proposed method in comparison with its baseline method. There were no significant changes in the output accuracy. On average, all of the tested datasets indicated 0.97% of improvement as opposed to their baseline techniques

فایل: ّFile: Download فایل