Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream

Al-Khateeb, Tahseen M.; Masud, Mohammad Mehedy; Al-Naami, Khaled; Şeker, Şadi Evren; Mustafa, Ahmad; Khan, Latifur; Trabelsi, Zouheir; Aggarwal, Charų C.; Han, Jiawei

doi:10.1109/tkde.2015.2507123

Cited by 41 publications

(22 citation statements)

References 22 publications

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“…OCLS [198] One-class learning and summarization ensemble UOCL [118] Extended ensemble for one-class learning and summarization IncOCBagg [102] Incremental one-class Bagging OLP [37] One-class ensemble based on prototypes Learn ++ .NC [130] Learn ++ ensemble for novel class detection ECSMiner [122] Ensemble for novelty detection with time constraints MCM [121] Ensemble for novelty detection and drifting feature space AnyNovel [1] Two-step clustering ensemble for novelty detection CBCE [171] Class-based ensemble for class evolution CLAM [4] Class-based micro classifier ensemble SCARN [4] Stream Classifier and novel and recurring class detector follow the previously seen distributions. Such examples may be caused by noise in the stream or may actually originate from a novel concept that started emerging.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…Two other ensemble-based approaches to novel class detection were proposed by Al-Khateeb et al [4] , namely Class Based Micro Classifier Ensemble (CLAM) and Stream Classifier And Novel and Recurring class detector (SCARN). CLAM uses an ensemble of micro-classifiers, where each base micro-classifier has been trained using only positive instances from a given class.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

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Ensemble learning for data stream analysis: A survey

et al. 2017

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In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semisupervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.

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Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Algorithm Descriptionmentioning

confidence: 99%

Ensemble learning for data stream analysis: A survey

et al. 2017

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“…In this method, the basic classifiers at leaves of trees adopt adaptive perceptions, which could deal with both multi-label classification and multi-target regression problems. An SVM based multi-label classification method has been introduced in [38], in which the objective function aims to minimize the ranking loss. Based on the Naïve Bayes, a multi-label adaptation algorithm was presented in [37]; In terms of association rule mining, a Multi-class, Multi-label Associative Classification approach, called MMAC [128], was presented for multi-label rule sets.…”

Section: B Algorithm Adaptationmentioning

confidence: 99%

A Survey on Multi-Label Data Stream Classification

Xiu-lin

Zhang

et al. 2020

IEEE Access

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Nowadays, many real-world applications of our daily life generate massive volume of streaming data at a higher speed than ever before, to name a few, Web clicking data streams, sensor network data and credit transaction streams. Contrary to traditional data mining using static datasets, there are several challenges for data stream mining, for instance, finite memory, one-pass and timely reaction. In this survey, we provide a comprehensive review of existing multi-label streams mining algorithms and categorize these methods based on different perspectives, which mainly focus on the multi-label data stream classification. We first briefly summarize existing multi-label and data stream classification algorithms and discuss their merits and demerits. Secondly, we identify mining constraints on classification for multi-label streaming data, and present a comprehensive study in algorithms for multi-label data stream classification. Finally, several challenges and open issues in multi-label data stream classification are discussed, which are worthwhile to be pursued by the researchers in the future. INDEX TERMS Data stream mining, multi-label data, multi-label classification.

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“…[31] CLAM 2016 It uses a class-based integrated classifier to efficiently classify data flow loop classes and novel classes, but it cannot classify multiclass data. [32]…”

Section: Awe 2003mentioning

confidence: 99%

A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

Zhou

Jin

et al. 2020

Journal of Electrical and Computer Engineering

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Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept evolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this paper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis distance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and novel class detection.

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Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream

Cited by 41 publications

References 22 publications

Ensemble learning for data stream analysis: A survey

Ensemble learning for data stream analysis: A survey

A Survey on Multi-Label Data Stream Classification

A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

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