Decision trees for mining data streams

Gama, João; Fernandes, Ricardo; Rocha, Ricardo Couto Antunes da

doi:10.3233/ida-2006-10103

Cited by 113 publications

(61 citation statements)

References 19 publications

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“…In the context of learning data streams, some proposed algorithms are capable of dealing with gradual concept drift [23], some can handle abrupt concept drift [4,6,12,13,31], and some have the potential to cope with both types [36]. However, most of these methods are appropriate only for supervised environments in which the labels of data are fully known.…”

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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“…The information maintained in Lr is similar to the sufficient statistics in VFDT [7] like algorithms. In [12] the authors present efficient algorithms to maintain Lr.…”

Section: Growing a Set Of Rulesmentioning

confidence: 99%

Very Fast Decision Rules for multi-class problems

Kosina

Gama

2012

Proceedings of the 27th Annual ACM Symposium on Applied Computing

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Decision rules are one of the most interpretable and flexible models for data mining prediction tasks. Till now, few works presented online, any-time and one-pass algorithms for learning decision rules in the stream mining scenario. A quite recent algorithm, the Very Fast Decision Rules (VFDR), learns set of rules, where each rule discriminates one class from all the other. In this work we extend the VFDR algorithm by decomposing a multi-class problem into a set of two-class problems and inducing a set of discriminative rules for each binary problem. The proposed algorithm maintains all properties required when learning from stationary data streams: online and any-time classifiers, processing each example once. Moreover, it is able to learn ordered and unordered rule sets. The new approach is evaluated on various real and artificial datasets. The new algorithm improves the performance of the previous version and is competitive with the state-of-the-art decision tree learning method for data streams.

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“…Overall, they should process examples at the rate they arrive, use a single scan of data and fixed memory, maintain a decision model at any time and be able to adapt the model to the most recent data. Successful data stream learning systems were already proposed for both prediction [11,12,22] and clustering [7,23]. All of them share the aim to produce reliable predictions or clusters.…”

Section: Learning From Data Streamsmentioning

confidence: 99%

Online Reliability Estimates for Individual Predictions in Data Streams

Rodrigues

Gama

Bosnić

2008

2008 IEEE International Conference on Data Mining Workshops

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Several predictive systems are nowadays vital for operations and decision support. The quality of these systems is most of the time defined by their average accuracy which has low or no information at all about the estimated error of each individual prediction. In many sensitive applications, users should be allowed to associate a measure of reliability to each prediction. In the case of batch systems, reliability measures have already been defined, mostly empirical measures as the estimation using the local sensitivity analysis. However, with the advent of data streams, these reliability estimates should also be computed online, based only on available data and current model's state. In this paper we define empirical measures to perform online estimation of reliability of individual predictions when made in the context of online learning systems. We present preliminary results and evaluate the estimators in two different problems.

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Decision trees for mining data streams

Cited by 113 publications

References 19 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

Very Fast Decision Rules for multi-class problems

Online Reliability Estimates for Individual Predictions in Data Streams

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