Learning with Drift Detection

Gama, João; Medas, Pedro; Castillo, Gladys; Rodrigues, Pedro Pereira

doi:10.1007/978-3-540-28645-5_29

Cited by 1,089 publications

(1,002 citation statements)

References 3 publications

Supporting

Mentioning

976

Contrasting

Unclassified

Order By: Relevance

“…(1) DDM [11], EDDM [12], EWMA [13]: Drift detection methods are used along with a incrementally updatable classifier which require all data samples to be labeled. (2) uMD (using Margin Density) [7], CDBD (Confidence Distribution Batch Detection) [6]: Using SVM as a classifier, it performs drift detection on unlabeled data streams.…”

Section: Experimental Results Under the Limited Access To Class Labelsmentioning

confidence: 99%

“…Under the assumption that all class labels are available immediately, the drift detection method (DDM) [11] detects changes based on the number of prediction errors yielded by the classification model. Using the confidence interval estimation for the average of the error rates, DDM defines an abrupt increase in the error rate as an occurrence of concept drift.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling

Kim

Park

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Section: Experimental Results Under the Limited Access To Class Labelsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling

Kim

Park

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

“…The SPC was presented by Gama et al [4] for change detection in the context of data streams. The principle motivating the detection of concept drift using the SPC is to trace the probability of the error rate for the streamed observations.…”

Section: Statistical Process Control (Spc)mentioning

confidence: 99%

On the Online Classification of Data Streams Using Weak Estimators

Tavasoli

Oommen

Yazidi

2016

Trends in Applied Knowledge-Based Systems and Data Science

View full text Add to dashboard Cite

Abstract. In this paper, we propose a novel online classifier for complex data streams which are generated from non-stationary stochastic properties. Instead of using a single training model and counters to keep important data statistics, the introduced online classifier scheme provides a real-time self-adjusting learning model. The learning model utilizes the multiplication-based update algorithm of the Stochastic Learning Weak Estimator (SLWE) at each time instant as a new labeled instance arrives. In this way, the data statistics are updated every time a new element is inserted, without requiring that we have to rebuild its model when changes occur in the data distributions. Finally, and most importantly, the model operates with the understanding that the correct classes of previously-classified patterns become available at a later juncture subsequent to some time instances, thus requiring us to update the training set and the training model. The results obtained from rigorous empirical analysis on multinomial distributions, is remarkable. Indeed, it demonstrates the applicability of our method on synthetic datasets, and proves the advantages of the introduced scheme.

show abstract

“…There is one class attribute that describes the "Poker Hand". Electricity is another widely used dataset described by M. Harries [11] and analysed by Gama [9]. This data was collected from the Australian New South Wales Electricity Market.…”

Section: Real-world Datamentioning

confidence: 99%

Fast Perceptron Decision Tree Learning from Evolving Data Streams

Bifet

Holmes

Pfahringer

et al. 2010

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees-Hoeffding Trees with naive Bayes models at the leaf nodes-albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron classifiers, while maintaining highly competitive accuracy. We also show that accuracy can be increased even further by combining majority vote, naive Bayes, and perceptrons. We evaluate four perceptron-based learning strategies and compare them against appropriate baselines: simple perceptrons, Perceptron Hoeffding Trees, hybrid Naive Bayes Perceptron Trees, and bagged versions thereof. We implement a perceptron that uses the sigmoid activation function instead of the threshold activation function and optimizes the squared error, with one perceptron per class value. We test our methods by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.

show abstract

Learning with Drift Detection

Cited by 1,089 publications

References 3 publications

An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling

An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling

On the Online Classification of Data Streams Using Weak Estimators

Fast Perceptron Decision Tree Learning from Evolving Data Streams

Contact Info

Product

Resources

About