Disposition-Based Concept Drift Detection and Adaptation in Data Stream

Agrahari, Supriya; Singh, Anil Kumar

doi:10.1007/s13369-022-06653-4

Cited by 9 publications

(2 citation statements)

References 38 publications

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“…Typical methods include the following: region concept drift detection [14], least squares density difference change detection [15], and equal density estimation method [16]. Multiple hypothesis testing detects concept drift in a number of different ways [17], such as typically based on the Hoeffding inequality hypothesis [18] and maximum likelihood estimation test approach [19].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Hybrid Ensemble Method for Accelerate Adaptation of Concept Drift

Wang

Guo

Zhang

et al. 2023

Preprint

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Because the insufficient new distribution training samples after concept drift occurs in streaming data, the performance of online learning model degrades and cannot quickly recover. Therefore, an adaptive hybrid ensemble method for accelerate adaptation of concept drift(AHE\_A$^2$CD) is proposed. After concept drift occurs, the proposed method extracts local information from the streaming data through the weighted base classifiers located in the classifier pool. The local information is supplemented into the current data block through expanding the data to make up for the lack of current distribution data after concept drift occurs and to build an efficient local base learner that conforms to the current data distribution. On this basis, the key data information at different stages is extracted by local base learner, and the current data is adaptively selected by the data distribution to construct diverse global base learner. Through the hybrid ensemble of the high-performance local base learner and the diverse global base learner, this method can adaptively learn the changing streaming data and improve the adaptability after concept drift occurs. Experimental results show that this method can accelerate the convergence of the online learning model after concept drift occurs and improve the real-time performance of streaming data classification.

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Section: Related Workmentioning

confidence: 99%

Adaptive Hybrid Ensemble Method for Accelerate Adaptation of Concept Drift

Wang

Guo

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…To get over the problem of false drift, [33] presented a Disposition Based Drift Detection Method (DBDDM), a DBDDM. In order to determine the actual drift, this study uses the approximation randomization test to calculate the frequency of successive drift and compares the frequency with the threshold.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

et al. 2022

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Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm's search for ways to improve the SDS's classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system's performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively. INDEX TERMSSpam detection system (SDS), grasshopper optimization algorithm (GOA), feature selection (FS), multi-objective optimization (MOO), multilayer perceptron (MLP)

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