A new method of mining data streams using harmony search

Karimi, Zohre; Abolhassani, Hassan; Beigy, Hamid

doi:10.1007/s10844-012-0199-2

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…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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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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“…Zohre Karimi et. al [7] proposed a new classification algorithm for the classification of batch data called harmony-based classifier and then give its incremental version for classification of data streams called incremental harmony-based classifier. Finally, improve it to reduce its computational overhead in absence of drifts and increase its robustness in presence of noise.…”

Section: Literature Surveymentioning

confidence: 99%

Review paper on adapting data stream mining concept drift using ensemble classifier approach

Motghare¹,

Mewada²

2014

IOSRJCE

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“…We explore the benefits of a bio-inspired solver for the construction of ensembles with different levels of diversity, in particular the Harmony Search (HS) algorithm [4]. HS has demonstrated to be competitive respect to other evolutionary heuristics for optimization paradigms in diverse fields such as energy [5,6], bio-informatics [7], telecommunications [8,9], data mining and concept drift [12], and logistics [13], among many others [10,11]. However, to the knowledge of the authors no previous contribution has gravitated on the diversity-accuracy trade-off in ensemble learning over nonstationary data.…”

Section: Introduction and Related Workmentioning

confidence: 99%

On the Creation of Diverse Ensembles for Nonstationary Environments Using Bio-inspired Heuristics

Lobo

Ser

Villar-Rodríguez

et al. 2017

Advances in Intelligent Systems and Computing

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Recently the relevance of adaptive models for dynamic data environments has turned into a hot topic due to the vast number of scenarios generating nonstationary data streams. When a change (concept drift) in data distribution occurs, the ensembles of models trained over these data sources are obsolete and do not adapt suitably to the new distribution of the data. Although most of the research on the field is focused on the detection of this drift to re-train the ensemble, it is widely known the importance of the diversity in the ensemble shortly after the drift in order to reduce the initial drop in accuracy. In a Big Data scenario in which data can be huge (and also the number of past models), achieving the most diverse ensemble implies the calculus of all possible combinations of models, which is not an easy task to carry out quickly in the long term. This challenge can be formulated as an optimization problem, for which bio-inspired algorithms can play one of the key roles in these adaptive algorithms. Precisely this is the goal of this manuscript: to validate the relevance of the diversity right after drifts, and to unveil how to achieve a highly diverse ensemble by using a self-learning optimization technique.

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A new method of mining data streams using harmony search

Cited by 13 publications

References 24 publications

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

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

Review paper on adapting data stream mining concept drift using ensemble classifier approach

On the Creation of Diverse Ensembles for Nonstationary Environments Using Bio-inspired Heuristics

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