An adaptive regression tree for non-stationary data streams

Gholipour, Ameneh; Hosseini, Mohammad Javad; Beigy, Hamid

doi:10.1145/2480362.2480519

Cited by 8 publications

(5 citation statements)

References 2 publications

(4 reference statements)

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“…There are many ways to improve the model when concept drift is detected. Some authors rebuild or update the model using the new batch of data (Gholipour et al 2013), while other remove some poorly performing nodes and grow new ones with the latest batch (Domingos and Hulten 2000;. In ensemble methods, some drop the worst model and replace it with a new model but with the other models unchanged.…”

Section: Related Workmentioning

confidence: 99%

Interval forecasts based on regression trees for streaming data

Zhao

Barber

Taylor

et al. 2019

Adv Data Anal Classif

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Section: Related Workmentioning

confidence: 99%

Interval forecasts based on regression trees for streaming data

Zhao

Barber

Taylor

et al. 2019

Adv Data Anal Classif

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show abstract

“…However, most of these methods are appropriate only for supervised environments in which the labels of data are fully known. Some single model classification techniques for data streams are proposed in [9,15,20,21]. Because they are building incrementally, they usually utilize only the most recent data.…”

Section: Related Workmentioning

confidence: 99%

An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams

2015

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Recent advances in storage and processing have provided the possibility of automatic gathering of information, which in turn leads to fast and continuous flows of data. The data which are produced and stored in this way are called data streams. Data streams are produced in large size, and much dynamism and have some unique properties which make them applicable to model many real data mining applications. The main challenge of streaming data is the occurrence of concept drift. In addition, regarding the costs of labeling of instances, it is often assumed that only a small fraction of instances are labeled. In this paper, we propose an ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment. Furthermore, this method is intended to recognize recurring concept drifts of data streams. In the proposed algorithm, a pool of classifiers is maintained by the algorithm with each classifier being representative of one single concept. At first, a batch of instances is classified by the algorithm. Thereafter, some of these instances are labeled and this partially labeled batch is used to update the classifiers in the pool. This process repeats for consecutive batches of the streams. The main advantage of the algorithm is that it uses unlabeled instances as well as labeled ones in the learning task. Experimental results show the effectiveness of the proposed algorithm over the state-of-the-art methods, in different aspects.

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“…True classification for a Global classifier from class d means generating output higher than CT for instances from class d, and lower than CT for instances of other classes. This accuracy can be calculated by using equation (5).…”

Section: Details About Classifiers and Updating Proceduresmentioning

confidence: 99%

“…Infinite lengths, high speed, limitation of response time and concept drift are challenges that we face in classifying data streams. There are many researches that address the aforementioned challenges [1,2,3,4,5,6,7,8,9]; however, most of them ignore another major challenge "concept evolution" which led to the emergence of novel classes.…”

Section: Introductionmentioning

confidence: 99%