Active Learning With Drifting Streaming Data

Žliobaitė, Indrė; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey

doi:10.1109/tnnls.2012.2236570

Cited by 316 publications

(275 citation statements)

References 15 publications

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“…This is far from real-life scenarios and would impose tremendous labeling costs on the system. Therefore, method for streaming data that reduce the cost of supervision (e.g., by active learning) is currently of crucial importance in this field [69]. This raises an open issue on how to sample imbalanced data streams?…”

Section: Learning From Imbalanced Data Streamsmentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

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Section: Learning From Imbalanced Data Streamsmentioning

confidence: 99%

Learning from imbalanced data: open challenges and future directions

2016

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“…liobaitė et al [15] presents a generic framework for active learning incrementally from drifting data streams. Several active learning strategies are incorporated into the framework.…”

Section: Related Workmentioning

confidence: 99%

“…OnPEAL is compared with the paired ensemble framework for active learning (PEFAL) presented in [16] and three representative active learning strategies described in [15], including Variable Uncertainty Strategy (VarUn), Uncertainty Strategy with Randomization (RanVarUn), and Split Strategy (Split). All the experiments are performed using the MOA data stream software suite [19].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…MOA is an open source software environment for stream data mining, including evaluation measures and a collection of implemented algorithms. For comparison, the PEFAL algorithm is implemented using MOA according to the procedure described in [16] and the active learning classifier using those strategies has already been integrated into MOA by authors of [15]. All algorithms use Hoeffding tree as base classifier to perform the classification and to produce the maximum a posteriori probability.…”

Section: Experimental Evaluationmentioning

confidence: 99%

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Online Active Learning with Drifted Data Streams Using Paired Ensemble Framework

et al. 2017

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Abstract. In learning to classify data streams, it is impractical and expensive to label all of the instances. Online active learning over streaming data poses additional challenges for its increasing volumes and concept drifts. We propose a new online paired ensemble active learning framework consisting of a stable classifier and a timely substituted dynamic classifier to react to different types of concept drifts. Classifiers are built in block based way and will learn new instances incrementally online. According to a combination strategy of uncertainty strategy and random strategy, the decision whether to label the incoming instance for the updating of the stable classifier and the dynamic classifier will be made. Experimental evaluation results on real datasets show the advantage of the proposed work in comparison with other approaches.

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“…Random strategy), queries are blindly chosen. The Random strategy (Zliobaite et al 2014) labels the incoming batches randomly instead of wisely deciding which batches are more informative. Constrained by budget, batches are sent for annotation.…”

Section: Unwise Strategymentioning

confidence: 99%

Learning from evolving video streams in a multi-camera scenario

2015

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Nowadays, video surveillance systems are taking the first steps toward automation, in order to ease the burden on human resources as well as to avoid human error. As the underlying data distribution and the number of concepts change over time, the conventional learning algorithms fail to provide reliable solutions for this setting. In this paper, we formalize a learning concept suitable for multi-camera video surveillance and propose a learning methodology adapted to that new paradigm. The proposed framework resorts to the universal background model to robustly learn individual object models from small samples and to more effectively detect novel classes. The individual models are incrementally updated in an ensemble-based approach, with older models being progressively forgotten. The framework is designed to detect and label new concepts automatically. The system is also designed to exploit active learning strategies, in order to interact wisely with operator, requesting assistance in the most ambiguous to classify observations. The experimental results obtained both on real and synthetic data sets verify the usefulness of the proposed approach.

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Active Learning With Drifting Streaming Data

Cited by 316 publications

References 15 publications

Learning from imbalanced data: open challenges and future directions

Learning from imbalanced data: open challenges and future directions

Online Active Learning with Drifted Data Streams Using Paired Ensemble Framework

Learning from evolving video streams in a multi-camera scenario

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