Document Details
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Document Type |
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Thesis |
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Document Title |
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Classification Model For Data Stream Mining With Concept Drift نموذج تصنيف للتنقيب في البيانات المتدفقة مع انحراف المفهوم |
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Subject |
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Faculty of Computing and Information Technology |
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Document Language |
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Arabic |
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Abstract |
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Data stream is the huge amount of non-stop and high-speed data generated in various fields, including financial processes, social media activities, Internet of Things applications, and many others. Such data cannot be processed through traditional data mining algorithms due to several constraints, including limited memory, data speed, and dynamic environment. Concept Drift is known as the main constraint of data stream mining, mainly in the classification task. It refers to the change in the data stream underlying distribution over time. Thus, it results in accuracy deterioration of classification models and wrong predictions. Spam emails, consumer behavior changes, and adversary activates, are examples of Concept Drift.
In this thesis, a Concept Drift Detection Model is introduced, Concept Drift Detection Model (CDDM). It monitors the accuracy of the classification model over a sliding window, assuming the decline in accuracy indicates a drift occurrence. A modification over CDDM is proposed and named W-CDDM.
Both models have evaluated against two real datasets and four artificial datasets. The experimental results of abrupt drift show that CDDM, W-CDDM outperforms the other models in the dataset of 100K and 1M instances, respectively. Regarding gradual drift, the W-CDDM overtakes the rest in terms of accuracy, run time, and detection delays in the dataset of 100 K instances. While in the dataset of 1M instances, CDDM has the highest accuracy using the NB classifier. Moreover, W-CDDM achieves the highest accuracy on real datasets. |
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Supervisor |
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Dr. Manal Abdulaziz Abdullah |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1441 AH
2020 AD |
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Added Date |
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Wednesday, May 27, 2020 |
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Researchers
| مشاعل شعيل الثبيتي | Al-Thabiti, Mashail Shaeel | Researcher | Master | |
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