Machine learning for streaming data

Gomes, Heitor Murilo; Read, Jesse; Bifet, Albert; Barddal, Jean Paul; Gama, João

doi:10.1145/3373464.3373470

Cited by 177 publications

(44 citation statements)

References 92 publications

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“…The problem is more aggravated in the case of event streams as the data points, i.e., events, are behaviorally connected. Hassani et al (2019), similar to the techniques prevalent in data stream processing such as summarization (Bahri et al, 2021;Gomes et al, 2019), suggested maintaining an abstract intermediate representation of the stream to be used as input for various process discovery techniques. Online conformance checking techniques in general assume unbounded memory.…”

Section: Related Workmentioning

confidence: 99%

Prefix Imputation of Orphan Events in Event Stream Processing

Zaman

Hassani

2021

Front. Big Data

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In the context of process mining, event logs consist of process instances called cases. Conformance checking is a process mining task that inspects whether a log file is conformant with an existing process model. This inspection is additionally quantifying the conformance in an explainable manner. Online conformance checking processes streaming event logs by having precise insights into the running cases and timely mitigating non-conformance, if any. State-of-the-art online conformance checking approaches bound the memory by either delimiting storage of the events per case or limiting the number of cases to a specific window width. The former technique still requires unbounded memory as the number of cases to store is unlimited, while the latter technique forgets running, not yet concluded, cases to conform to the limited window width. Consequently, the processing system may later encounter events that represent some intermediate activity as per the process model and for which the relevant case has been forgotten, to be referred to as orphan events. The naïve approach to cope with an orphan event is to either neglect its relevant case for conformance checking or treat it as an altogether new case. However, this might result in misleading process insights, for instance, overestimated non-conformance. In order to bound memory yet effectively incorporate the orphan events into processing, we propose an imputation of missing-prefix approach for such orphan events. Our approach utilizes the existing process model for imputing the missing prefix. Furthermore, we leverage the case storage management to increase the accuracy of the prefix prediction. We propose a systematic forgetting mechanism that distinguishes and forgets the cases that can be reliably regenerated as prefix upon receipt of their future orphan event. We evaluate the efficacy of our proposed approach through multiple experiments with synthetic and three real event logs while simulating a streaming setting. Our approach achieves considerably higher realistic conformance statistics than the state of the art while requiring the same storage.

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Section: Related Workmentioning

confidence: 99%

Prefix Imputation of Orphan Events in Event Stream Processing

Zaman

Hassani

2021

Front. Big Data

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“…This can be an abrupt event in the data stream, but it also can be a gradual, reoccurring or even virtual process [26]. Gomes et al [27] mention other possible research directions within incremental learning like anomaly detection [28], ensemble learning, recurrent neural networks and reinforcement learning.…”

Section: Related Workmentioning

confidence: 99%

Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models

Limberg

Wersing²,

Ritter

2020

MAKE

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For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching. Cross-validation and interleaved test/train error are here the standard supervised approaches. We propose a novel semi-supervised accuracy estimation approach that clearly outperforms these two methods. We introduce the Configram Estimation (CGEM) approach to predict the accuracy of any classifier that delivers confidences. By calculating classification confidences for unseen samples, it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on novel data in a semi-supervised fashion. We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning. The results show that our novel method improves accuracy estimation over standard methods and requires less supervised training data after deployment of the model. We demonstrate the application of our approach to a challenging robot object recognition task, where the human teacher can use our method to judge sufficient training.

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“…Although much work has been done on concept drift [ 5 – 7 ], the class imbalance problem [ 11 ] (i.e., negative class instances are more extensive than other classes) further increases the difficulty of addressing concept drift [ 12 ]. Class imbalance commonly exists in the real world.…”

Section: Introductionmentioning

confidence: 99%

Cost‐Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

Sun

et al. 2021

Computational Intelligence and Neuroscience

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Class imbalance and concept drift are two primary principles that exist concurrently in data stream classification. Although the two issues have drawn enough attention separately, the joint treatment largely remains unexplored. Moreover, the class imbalance issue is further complicated if data streams with concept drift. A novel Cost-Sensitive based Data Stream (CSDS) classification is introduced to overcome the two issues simultaneously. The CSDS considers cost information during the procedures of data preprocessing and classification. During the data preprocessing, a cost-sensitive learning strategy is introduced into the ReliefF algorithm for alleviating the class imbalance at the data level. In the classification process, a cost-sensitive weighting schema is devised to enhance the overall performance of the ensemble. Besides, a change detection mechanism is embedded in our algorithm, which guarantees that an ensemble can capture and react to drift promptly. Experimental results validate that our method can obtain better classification results under different imbalanced concept drifting data stream scenarios.

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Machine learning for streaming data

Cited by 177 publications

References 92 publications

Prefix Imputation of Orphan Events in Event Stream Processing

Prefix Imputation of Orphan Events in Event Stream Processing

Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models

Cost‐Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance

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