Alert Generation Framework from Twitter Data Stream During Disaster Events

Hasla, M.; Swaraj, K. P.

doi:10.1007/978-3-030-37051-0_60

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Nowadays, as new technologies and requirements develop, instead of static data, we are faced with streams of data that are dynamic and are produced continuously. Data collected from IoT sensors [1,2], medical systems [3][4][5], computer networks traffic [6,7], credit card transactions [8,9], and daily tweets on the Twitter [10][11][12] are examples of data streams in the modern society. A data stream is an infinite sequence of consecutive data instances over time that may be generated at high speed.…”

Section: Introductionmentioning

confidence: 99%

Rarity updated ensemble with oversampling: An ensemble approach to classification of imbalanced data streams

Nouri,

Kiani,

Fadishei

2024

Statistical Analysis

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Today's ever‐increasing generation of streaming data demands novel data mining approaches tailored to mining dynamic data streams. Data streams are non‐static in nature, continuously generated, and endless. They often suffer from class imbalance and undergo temporal drift. To address the classification of consecutive data instances within imbalanced data streams, this research introduces a new ensemble classification algorithm called Rarity Updated Ensemble with Oversampling (RUEO). The RUEO approach is specifically designed to exhibit robustness against class imbalance by incorporating an imbalance‐specific criterion to assess the efficacy of the base classifiers and employing an oversampling technique to reduce the imbalance in the training data. The RUEO algorithm was evaluated on a set of 20 data streams and compared against 14 baseline algorithms. On average, the proposed RUEO algorithm achieves an average‐accuracy of 0.69 on the real‐world data streams, while the chunk‐based algorithms AWE, AUE, and KUE achieve average‐accuracies of 0.48, 0.65, and 0.66, respectively. The statistical analysis, conducted using the Wilcoxon test, reveals a statistically significant improvement in average‐accuracy for the proposed RUEO algorithm when compared to 12 out of the 14 baseline algorithms. The source code and experimental results of this research work will be publicly available at https://github.com/vkiani/RUEO.

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