Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data

Li, Peipei; Wu, Man; He, Junjia; Hu, Xuegang

doi:10.1007/s00354-021-00126-2

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…Generally, active learning methods are used to reduce annotation costs [32]. Unlike active learning with other learners, active learning with SVM (AL-SVM) selects the informative samples closest to the current model's hyperplane [11,33] incrementally. Due to this behavior, AL-SVM is used as an informative undersampling criterion [34] for solving standalone class imbalance problems.…”

Section: P(y/x) Drift Detection Methodsmentioning

confidence: 99%

A New Large Scale SVM for Classification of Imbalanced Evolving Streams

Himaja

Dondeti

Srilakshmi

2022

IJRITCC

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Classification from imbalanced evolving streams possesses a combined challenge of class imbalance and concept drift (CI-CD). However, the state of imbalance is dynamic, a kind of virtual concept drift. The imbalanced distributions and concept drift hinder the online learner’s performance as a combined or individual problem. A weighted hybrid online oversampling approach,”weighted online oversampling large scale support vector machine (WOOLASVM),” is proposed in this work to address this combined problem. The WOOLASVM is an SVM active learning approach with new boundary weighing strategies such as (i) dynamically oversampling the current boundary and (ii) dynamic weighing of the cost parameter of the SVM objective function. Thus at any time step, WOOLASVM maintains balanced class distributions so that the CI-CD problem does not hinder the online learner performance. Over extensive experiments on synthetic and real-world streams with the static and dynamic state of imbalance, the WOOLASVM exhibits better online classification performances than other state-of-the-art methods.

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Section: P(y/x) Drift Detection Methodsmentioning

confidence: 99%

A New Large Scale SVM for Classification of Imbalanced Evolving Streams

Himaja

Dondeti

Srilakshmi

2022

IJRITCC

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“…The drift detection can be carried out by hypothesis tests [21,22], change-point method [23], sequential hypothesis test [24], and change detection test [23]. Recently, statistical methods that identify distribution differences have been used in SDDM [25] for drift detection, while cluster-based distance methods [26] has been used to detect recurring CDs. Drift detection methods such as the drift detection method for OCI (DDM-OCI) [27], LFR [28], and PAUC [29], on the other hand, detect p(y/x) drift in imbalanced distributions.…”

Section: Learning Streams From Non-stationary Environmentsmentioning

confidence: 99%

Analyzing Adaptive and Non-Adaptive Online Learners on Imbalanced Evolving Streams

Himaja¹,

Dondeti²,

Srilakshmi³

2023

IJCAI

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The online class imbalance and concept drift (OCI-CD) has recently received much interest. The impact of this combined problem on state-of-the-art of online adaptive and non-adaptive learners has received little attention. This study investigates the effect of parameters such as current imbalance ratio, stream length, drift type, drift levels, and imbalance state (static or dynamic) on adaptive and non-adaptive online learners. The experimental results show that each parameter considered for the study has a significant impact on learner performance: (a) minority class performance decreases as the degree of imbalance increases, (b) non-adaptive learners are much susceptible to class imbalance, concept drift, and the combined problem of both drifts than adaptive learners, (c) adaptive learners are only susceptible to class imbalance drifts, and (d) the impact of the dynamic degree of imbalance is more on learner than static (e) the adaptive large scale support vector machine yields stable performance to all the parameters considered for the study. Based on these findings, directions for developing new approaches are also presented. Povzetek: Analizirane so razne metode strojnega učenja glede na parametre učenja, recimo spreminjanje neuravnoteženja razredov.

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