A streaming ensemble algorithm (SEA) for large-scale classification

Street, W. Nick; Kim, YongSeog

doi:10.1145/502512.502568

Cited by 958 publications

(664 citation statements)

References 19 publications

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“…To overcome this drawback, an evaluation of the change detection algorithms was performed using the SEA concepts data set [38], a benchmark problem for concept drift. Figure 14 shows the error rate (computed using a naive-Bayes classifier), which presents three drifts.…”

Section: Experiments On a Public Data Setmentioning

confidence: 99%

Fading histograms in detecting distribution and concept changes

Sebastião

Gama

Mendonça

2017

Int J Data Sci Anal

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The remarkable number of real applications under dynamic scenarios is driving a novel ability to generate and gather information. Nowadays, a massive amount of information is generated at a high-speed rate, known as data streams. Moreover, data are collected under evolving environments. Due to memory restrictions, data must be promptly processed and discarded immediately. Therefore, dealing with evolving data streams raises two main questions: (i) how to remember discarded data? and (ii) how to forget outdated data? To maintain an updated representation of the time-evolving data, this paper proposes fading histograms. Regarding the dynamics of nature, changes in data are detected through a windowing scheme that compares data distributions computed by the fading histograms: the adaptive cumulative windows model (ACWM). The online monitoring of the distance between data distributions is evaluated using a dissimilarity measure based on the asymmetry of the Kullback-Leibler divergence. The experimental results support the ability of fading histograms in providing an updated representation of data. Such property works in favor of detecting distribution changes with smaller detection delay time when compared with standard histograms. With respect to the detection of concept changes, the ACWM is compared with 3 known algorithms taken from the literature, using artificial data and using public data sets, presenting better results. Furthermore, we the proposed method was extended for multidimensional and the experiments performed show the ability of the ACWM for detecting distribution changes in these settings.

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Section: Experiments On a Public Data Setmentioning

confidence: 99%

Fading histograms in detecting distribution and concept changes

Sebastião

Gama

Mendonça

2017

Int J Data Sci Anal

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“…This artificial dataset contains abrupt concept drift, first introduced in [23]. It is generated using three attributes, where only the two first attributes are relevant.…”

Section: Sea Conceptsmentioning

confidence: 99%

Improving Adaptive Bagging Methods for Evolving Data Streams

Bifet

Holmes

Pfahringer

et al. 2009

Lecture Notes in Computer Science

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Abstract. We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.

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“…A substantial amount of recent work has focused on continuous mining of data streams [4,10,11,15,16]. Typical applications include network traffic monitoring, credit card fraud detection and sensor network management systems.…”

Section: Introductionmentioning

confidence: 99%

“…Previous ensemble methods for drifting data streams have primarily relied on bagging-style techniques [15,16]. Street et al [15] gave an ensemble algorithm that builds one classifier per data block independently.…”

Section: Introductionmentioning

confidence: 99%

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Fast and Light Boosting for Adaptive Mining of Data Streams

Chu

Zaniolo

2004

Advances in Knowledge Discovery and Data Mining

108

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Abstract. Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept drift. We propose a novel boosting ensemble method that achieves these objectives. The technique is based on a dynamic sample-weight assignment scheme that achieves the accuracy of traditional boosting without requiring multiple passes through the data. The technique assures faster learning and competitive accuracy using simpler base models. The scheme is then extended to handle concept drift via change detection. The change detection approach aims at significant data changes that could cause serious deterioration of the ensemble performance, and replaces the obsolete ensemble with one built from scratch. Experimental results confirm the advantages of our adaptive boosting scheme over previous approaches.

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A streaming ensemble algorithm (SEA) for large-scale classification

Cited by 958 publications

References 19 publications

Fading histograms in detecting distribution and concept changes

Fading histograms in detecting distribution and concept changes

Improving Adaptive Bagging Methods for Evolving Data Streams

Fast and Light Boosting for Adaptive Mining of Data Streams

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