SOM-based partial labeling of imbalanced data stream

Arabmakki, Elaheh; Kantardzic, Mehmed

doi:10.1016/j.neucom.2016.11.088

Cited by 20 publications

(6 citation statements)

References 16 publications

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“…For EV applications, battery aging will result in the degradation of battery capacity and the increase of battery internal resistance. Thus, the battery SOH can be estimated by the internal resistance or usable capacity as a kind of prediction regime changes in computer science field [54]. Numerous approaches have been proposed to estimate battery SOH, which are categorized into three groups, namely, model-free, model-based, and data mining methods.…”

Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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Batteries have been widely applied in many high-power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro-thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

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Section: Soh Estimationmentioning

confidence: 99%

A brief review on key technologies in the battery management system of electric vehicles

et al. 2018

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“…Arabmakki and Kantardzic [65] proposed the RLS-SOM (Reduced labeled Samples-Self Organizing Map) framework for imbalanced stream. An ensemble classifies with DWM and retrains a new model using partial labeled samples when a drift is detected.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If it is a conditional drift, SVM is employed to choose the closest instances of decision boundary (margin). However, if no minority instance is found on decision boundary, SOM algorithm maps the batch to search for minority instances in the whole feature space [65].…”

Section: Literature Reviewmentioning

confidence: 99%

An Overview on Concept Drift Learning

2019

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Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, such as new classes, clusters, and features. Traditional classifiers can be easily fooled in such situations, resulting in poor performances. Common concept drift domains include recommendation systems, energy consumption, artificial intelligence systems with dynamic environment interaction, and biomedical signal analysis (e.g., neurogenerative diseases). In this paper, we surveyed several works that deal with concept drift, as well as we presented a comprehensive study of public synthetic and real datasets that can be used to cope with such a problem. In addition, we considered a review of different types of drifts and approaches to handling such changes in the data. We considered different learners employed in classification tasks and the use of drift detection mechanisms, among other characteristics. INDEX TERMS Concept drift, machine learning, pattern recognition.

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“…If x is the feature vector, and y is the class label , the concept drift is defined as changes in p(x, y) in the following joint probability distribution for the classification problem (Gao, Fan, Han, & Philip, 2007): (1) Here, ) (x p is the feature probability and …”

Section: Concept Driftmentioning

confidence: 99%

“…At the beginning, several documents are labeled to build an initial model G with some neurons. Each new document then is presented as a feature vector x, and shown in (1).…”

Section: Semi Supervised-labeled and Unlabeled Datamentioning

confidence: 99%

A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

Arabmakki¹

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SOM-based partial labeling of imbalanced data stream

Cited by 20 publications

References 16 publications

A brief review on key technologies in the battery management system of electric vehicles

A brief review on key technologies in the battery management system of electric vehicles

An Overview on Concept Drift Learning

A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

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