eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams

Stahl, Frederic; Gaber, Mohamed Medhat; Salvador, Manuel Martin

doi:10.1007/978-1-4471-4739-8_5

Cited by 6 publications

(14 citation statements)

References 20 publications

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“…A notable family of algorithms that follows the alternative ‘separate and conquer’ approach is the Prism family of algorithms (Cendrowska, ; Bramer, ; Bramer, ). A recent development of the Prism family is the parallelization of Prism including a parallelized pre‐pruning facility (Stahl et al ., ), an improved pre‐pruning method, Jmax‐pruning (Stahl and Bramer, ) and an adaptive version of Prism for data stream classification called ‘eRules’ (Stahl et al ., ). Generally, members of the Prism family produce a comparable classification accuracy to decision trees.…”

Section: Related Workmentioning

confidence: 97%

Random Prism: a noise‐tolerant alternative to Random Forests

Stahl

Bramer

2013

Expert Systems

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Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

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Section: Related Workmentioning

confidence: 97%

Random Prism: a noise‐tolerant alternative to Random Forests

Stahl

Bramer

2013

Expert Systems

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“…A recent data stream classifier development is eRules [16]. eRules is based on a sliding window approach [3] and uses the rule-based Prism classifier [7] to work on streaming data.…”

Section: Introductionmentioning

confidence: 99%

“…eRules is based on a sliding window approach [3] and uses the rule-based Prism classifier [7] to work on streaming data. eRules has shown good classification accuracy and adaptability to concept drifts [16]. eRules induces rules of the form IF condition THEN classification that are expressive and compact and effectively represent information for classification.…”

Section: Introductionmentioning

confidence: 99%

“…: medical and financial applications. Further weaknesses of the decision tree structure in comparison with rulesets have been analysed, and the reader is referred to [16] for further reading.…”

Section: Introductionmentioning

confidence: 99%

“…Among these techniques, possibly the best established methods, are the Hoeffding tree based approaches [9]. Nevertheless, tree based classification approaches have been criticised in the past for both static data classification [7,20] as well as for data stream classification [16,19]. In particular, the drawbacks of decision tree based techniques in dealing with concept drift are mentioned in [19] and the fact that decision trees cannot abstain from a potentially wrong classification are discussed in [16].…”

Section: Introductionmentioning

confidence: 99%

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Computationally Efficient Rule-Based Classification for Continuous Streaming Data

Stahl

Gomes

et al. 2014

Research and Development in Intelligent Systems XXXI

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Advances in hardware and software technologies allow to capture streaming data. The area of Data Stream Mining (DSM) is concerned with the analysis of these vast amounts of data as it is generated in real-time. Data stream classification is one of the most important DSM techniques allowing to classify previously unseen data instances. Different to traditional classifiers for static data, data stream classifiers need to adapt to concept changes (concept drift) in the stream in real-time in order to reflect the most recent concept in the data as accurately as possible. A recent addition to the data stream classifier toolbox is eRules which induces and updates a set of expressive rules that can easily be interpreted by humans. However, like most rule-based data stream classifiers, eRules exhibits a poor computational performance when confronted with continuous attributes. In this work, we propose an approach to deal with continuous data effectively and accurately in rule-based classifiers by using the Gaussian distribution as heuristic for building rule terms on continuous attributes. We show on the example of eRules that incorporating our method for continuous attributes indeed speeds up the real-time rule induction process while maintaining a similar level of accuracy compared with the original eRules classifier. We termed this new version of eRules with our approach G-eRules.

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Data stream classification with ant colony optimisation

Helal

Otero

2022

Int J of Intelligent Sys

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Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO‐based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO‐based algorithm for data stream classification. The proposed algorithm, called Stream Ant‐Miner (sAnt‐Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed‐type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt‐Miner produces statistically significant concise models compared with state‐of‐the‐art rule induction data stream algorithms, without negative effects on their predictive accuracy.

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eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams

Cited by 6 publications

References 20 publications

Random Prism: a noise‐tolerant alternative to Random Forests

Random Prism: a noise‐tolerant alternative to Random Forests

Computationally Efficient Rule-Based Classification for Continuous Streaming Data

Data stream classification with ant colony optimisation

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