Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model

Ancy, S.; Paulraj, D.

doi:10.1016/j.comcom.2020.01.061

Cited by 34 publications

(15 citation statements)

References 13 publications

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“…The most popular approach lies in combining resampling techniques with Online Bagging (Wang et al, 2015Wang and Pineau, 2016). Similar strategies can be applied to Adaptive Random Forest (Gomes et al, 2017), Online Boosting (Klikowski and Woźniak, 2019;Gomes et al, 2019) 2017), Dynamic Feature Selection (Wu et al, 2014), Adaptive Random Forest with resampling (Ferreira et al, 2019), Kappa Updated Ensemble (Cano and Krawczyk, 2020), Robust Online Self-Adjusting Ensemble (Cano and Krawczyk, 2022) or any ensemble that can incrementally update its base learners (Ancy and Paulraj, 2020;Li et al, 2020). It is interesting to note that preprocessing approaches enhance diversity among base classifiers (Zyblewski et al, 2019).…”

Section: Ensembles For Imbalanced Data Streamsmentioning

confidence: 99%

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Aguiar¹,

Krawczyk²,

Cano³

2022

Preprint

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Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.

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Section: Ensembles For Imbalanced Data Streamsmentioning

confidence: 99%

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Aguiar¹,

Krawczyk²,

Cano³

2022

Preprint

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“…Similar to the ranked-based approach, a heterogeneous dynamic weighted majority [20] method is also applied to the ensemble modeling. Ancy and Paulraj [39] have used dynamic sampling on their proposed ensemble model to handle imbalanced data with concept drift. A mixture of neural network and support vector machine-based ensemble methods using transfer learning and incremental learning is developed [21] to lower the effects of concept drift.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Mizan,

Taghipour

2022

Scientia Iranica

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Real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. The erroneous data causes inaccurate forecasting which leads to business loss. Moreover, the concept drift problem is a known problem in time series forecasting that also results in poor forecasting accuracy. The execution time of a machine learning model is also crucial when it is deployed in a real-time environment. This work presents an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the timevariant windowing technique. A data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. A meta-model is developed in the online forecasting phase which ensures faster execution for incoming data. The model is implemented for the medical workload prediction after testing and comparing with a few other heterogeneous ensemble models. The comparison results show in terms of the root mean squared error, the proposed model performs at least 65.7% better than the heterogeneous stacked ensemble models on the experimental dataset. Moreover, in comparison to the other standalone models considered in this experiment, the ABRE model reduces the prediction error by approximately 73.6%.

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“…Several chunk-based approaches have been proposed based on the storage of old minority class examples to help overcoming class imbalance [1,14,15,27,28,35,45,82]. Some of these approaches train components by combining all minority class examples seen so far with majority examples from the most recent chunk [27,28,45,82].…”

Section: Classifiers For Imbalanced Data Streamsmentioning

confidence: 99%

“…A detailed description of the generator is given in Section A of the supplementary materials. 1 The source code of a MOA [2] compatible implementation of the generator is available at: https://github.com/dabrze/imbalanced-stream-generator.…”

Section: Experimental Aims and Setupmentioning

confidence: 99%

The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

et al. 2021

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Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers’ reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.

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Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model

Cited by 34 publications

References 13 publications

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

The impact of data difficulty factors on classification of imbalanced and concept drifting data streams

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