Applying Fourier Inspired Windows for Concept Drift Detection in Data Stream

Misra, Sumit; Biswas, Dipan; Saha, Sanjoy Kumar; Mazumdar, Chandan

doi:10.1109/calcon49167.2020.9106537

Cited by 4 publications

(1 citation statement)

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“…In addition, the changes in error rates may take gradual processes and the false predicted instances must be accumulated enough before a concept drift can be detected. To overcome these problems, this paper proposed a novel concept drift detection method by employing a two-window strategy to compare the data distribution across two consecutive windows that are considered the most prevalent method based on the literature review [34,35].…”

Section: Drift Detection-based On Adaptive Window Modelmentioning

confidence: 99%

Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification

Rabiu¹,

Salim²,

Nasser³

et al. 2023

Computers, Materials &Amp; Continua

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Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique, an ensemble classifier based on drift detection for opinion mining and sentiment classification. To improve classification performance, this approach uses four different dissimilarity measures to determine the degree of concept drifts in the data stream. Whenever a drift is detected, the proposed method builds and adds a new classifier to the ensemble. To add a new classifier, the total number of classifiers in the ensemble is first checked if the limit is exceeded before the classifier with the least weight is removed from the ensemble. To this end, a weighting mechanism is used to calculate the weight of each classifier, which decides the contribution of each classifier in the final classification results. Several experiments were conducted on real-world datasets and the results were evaluated on the false positive rate, miss detection rate, and accuracy measures. The proposed method is also compared with the state-of-the-art methods, which include DDM, EDDM, and PageHinkley with support vector machine (SVM) and Naïve Bayes classifiers that are frequently used in concept drift detection studies. In all cases, the results show the efficiency of our proposed method.

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Section: Drift Detection-based On Adaptive Window Modelmentioning

confidence: 99%