Combining similarity in time and space for training set formation under concept drift

Žliobaitė, Indrė

doi:10.3233/ida-2011-0484

Cited by 50 publications

(30 citation statements)

References 24 publications

(34 reference statements)

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“…-Luxembourg [Zliobaite, 2011] is constructed by using European Social Survey data. There are 1,900 instances with 32 attributes in total, and each instance is an individual and attributes are formed from answers to the survey questionnaire.…”

Section: E2 Descriptions Of Real-world Datasetsmentioning

confidence: 99%

Handling concept drift via model reuse

2019

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In many real-world applications, data are often collected in the form of stream, and thus the distribution usually changes in nature, which is referred as concept drift in literature. We propose a novel and effective approach to handle concept drift via model reuse, leveraging previous knowledge by reusing models. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the model performance. We provide generalization and regret analysis. Experimental results also validate the superiority of our approach on both synthetic and real-world datasets.

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Section: E2 Descriptions Of Real-world Datasetsmentioning

confidence: 99%

Handling concept drift via model reuse

2019

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“…If the fluctuations exceed the predefined statistical bounds, a drift adaptation process is triggered. Other novel drift adaptation algorithms are : a) recurrence drift adaptation : SAMkNN [44], Just-in-time classifiers for recurrent concepts [1] ; b) active learning algorithms [38] ; c) time dependency algorithms : FISH1, FISH2, FISH3 [37] and some famous concept-drift friendly incremental learners, such as Concept-adapting Very Fast Decision Tree (CVFDT) [27].…”

Section: Concept Drift Detection and Adaptation Algorithmsmentioning

confidence: 99%

Accumulating regional density dissimilarity for concept drift detection in data streams

Liu

Zhang

2018

Pattern Recognition

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In a non-stationary environment, newly received data may have different knowledge patterns to the data used to train learning models. As time passes, the performance of learning models becomes increasingly unreliable. This problem is known as concept drift and is a common issue in real-world domains. Concept drift detection has attracted increasing attention in recent years, however, hardly any existing methods pay attention to small regional drifts, and their drift detection accuracy may vary due to different statistical significance test. To address these problems, this paper presents a novel concept drift detection method that is based on regional density estimation, named nearest neighbor-based density variation identification (NN-DVI). It consists of three components. The first one is a k-nearest neighborbased space partitioning schema (NNPS) which transforms unmeasurable discrete data instances into a set of shared subspaces for density estimation. The second one is a distance function that accumulates the density discrepancies in these subspaces and quantifies the overall discrepancies. The last component is a tailored statistical significant test by which the confidence interval of a concept drift can be accurately determined. The distance applied in NN-DVI is sensitive to regional drift, and has been proven to follow a normal distribution. As a result, both the accuracy and false alarm rate of NN-DVI are statistically guaranteed. In addition, several benchmarks have been used to evaluate the method, including both synthetic and real-world datasets. The overall results show that NN-DVI has better performance in terms of addressing concept-drift-detection-related problems.

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“…Indrė Žliobaitė [58] invented a family of training set formation methods named FISH (uniFied Instance Selection algoritHm). The FISH family dynamically selects a set of relevant instances as the training set for the current target instance.…”

Section: Case-based Reasoning For Handling Concept Driftmentioning

confidence: 99%

“…We compare our NEFCS algorithm with other CBM approaches designed for time-varying tasks, including IB3 [50], PECS [53] and FISH2 [58], and for static tasks, including BBNR [5] c This is the same method of constructing the competence model as described in ICF [49]. To compare case distribution, the window size selected is half the training set size, that is, 450, so that change detection can be started from the outset.…”

Section: Evaluating Nefcsmentioning

confidence: 99%

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A concept drift-tolerant case-base editing technique

Ning

Zhang

et al. 2016

Artificial Intelligence

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The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks.

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Combining similarity in time and space for training set formation under concept drift

Cited by 50 publications

References 24 publications

Handling concept drift via model reuse

Handling concept drift via model reuse

Accumulating regional density dissimilarity for concept drift detection in data streams

A concept drift-tolerant case-base editing technique

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