Classification of Uncertain Data Streams Based on Extreme Learning Machine

Cao, Keyan; Wang, Guoren; Han, Deqiang; Ning, Jingwei; Zhang, Xin

doi:10.1007/s12559-014-9279-7

Cited by 31 publications

(13 citation statements)

References 27 publications

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“…In this field, supervised learning has been used based on factors such as QoS and automatic configuration. For example, QoS has been used to create resource appraisal and classification models for IoT [ 87 ] or for any type of network [ 88 ]. It has also been used as a retroactive measure to tailor service selection [ 89 ] and for decreasing the time required by virtual machine migration processes through WAN links [ 90 ].…”

Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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“…Whereas, modularity feature of ensembling makes it more feasible to adapt to any new concept during online learning. For example, a study (Cao et al, 2015;Khamassi et al, 2015) proposed the ELM based Weighted Ensemble Classifier to adjust the classifier after observing the concept drift issue dynamically. Block-Based Ensemble Approach (Brzezinski and Stefanowski, 2012) and Weighting Data Ensemble Approach (Sidhu and Bhatia, 2018) are the two most effective available approaches.…”

Section: Ensembles Classifiers Based CD Adaptation Approachesmentioning

confidence: 99%

Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Hashmani

Jameel

Rehman

2020

International Journal on Smart Sensing and Intelligent Systems

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Concept Drift's issue is a decisive problem of online machine learning, which causes massive performance degradation in the analysis. The Concept Drift is observed when data's statistical properties vary at a different time step and deteriorate the trained model's accuracy and make them ineffective. However, online machine learning has significant importance to fulfill the demands of the current computing revolution. Moreover, it is essential to understand the existing Concept Drift handling techniques to determine their associated pitfalls and propose robust solutions. This study attempts to summarize and clarify the empirical pieces of evidence of the Concept Drift issue and assess its applicability to meet the current computing revolution. Also, this study provides a few possible research directions and practical implications of Concept Drift handling.

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“…To the best of our knowledge, no previous single base ELM approach specifically addresses many concept drifts learning [ 6 ]. However, some papers [ 23 , 24 ] already discussed how the ELM is implemented in adaptive environment.…”

Section: Related Workmentioning

confidence: 99%

“…Cao et al [ 24 ] proposed two-phase classification algorithm: first, weighted ensemble classifier based on ELM (WEC-ELM) algorithm, which can dynamically adjust classifier and the weight of training uncertain data to solve the problem of concept drift, and second, an uncertainty classifier based on ELM (UC-ELM) algorithm designed for the classification of unknown data streams, which considers attribute (tuple) value and its uncertainty, thus improving the efficiency and accuracy. When concept drift occurs, WEC-ELM will dynamically adjust the classifiers and the weight of training data, thus a new classifier will be added to the ensemble until it reached a preset maximum and then removed the worst-performing classifier.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Online Sequential ELM for Concept Drift Tackling

Budiman

Fanany

Basaruddin

2016

Computational Intelligence and Neuroscience

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A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.

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Classification of Uncertain Data Streams Based on Extreme Learning Machine

Cited by 31 publications

References 27 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Concept Drift Evolution In Machine Learning Approaches: A Systematic Literature Review

Adaptive Online Sequential ELM for Concept Drift Tackling

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