CDDM: Concept Drift Detection Model for Data Stream
Dublin Core
Title
CDDM: Concept Drift Detection Model for Data Stream
Subject
Data Stream Mining
Concept Drift
Concept Drift Detection
Data Stream Classification.
Description
Data stream is the huge amount of 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 underlining 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 paper, 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 a weighted version of the CDDM as 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 overtook 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 got the highest accuracy using the NB classifier. Moreover, W-CDDM achieves the highest accuracy on real datasets.
Creator
Althabiti, Mashail Shaeel
Abdullah, Manal
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 10 (2020); pp. 90-106
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2020-06-30
Rights
Copyright (c) 2020 Mashail Shaeel Althabiti, Manal Abdullah
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Mashail Althabiti Shaeel and Manal Abdullah, CDDM: Concept Drift Detection Model for Data Stream, International Association of Online Engineering (IAOE), Vienna, Austria, 2020, accessed November 7, 2024, https://igi.indrastra.com/items/show/1737