Adaptive SVM for Data Stream Classification

Abdulkarim, Salihu Aish

doi:10.18489/sacj.v29i1.414

Cited by 7 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of online hyperparameter tuning algorithms, there are few works [17,[23][24][25][26] that use support vector machines together with batch processing, gradient solutions combined with brute force or genetic algorithms to optimise hyperparameters. Lawal and Abdulkarim [23] introduce an incremental learning-model selection method for data stream batches. It relies on incremental k-fold cross-validation to perform the incremental tuning of the support vector machine hyperparameters.…”

Section: Related Workmentioning

confidence: 99%

Hyperparameter self-tuning for data streams

et al. 2021

View full text Add to dashboard Cite

The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. The online processing of these data streams therefore requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, streambased learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, that frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder-Mead algorithm to dynamically-sized samples, converging to optimal settings in a single-pass over data, while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results show that SSPT converges significantly faster and

show abstract

Section: Related Workmentioning

confidence: 99%

Hyperparameter self-tuning for data streams

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the literature, we can also find several works proposing incremental model selection for classification tasks. The IL-MS algorithm [23] uses a k-fold crossvalidation procedure to compute additional support vector machine (SVM) models with different configurations to select the best model. This procedure is run periodically, to minimize the computational cost; however, the evaluation procedure performs a double pass over the data (offline learning), which increases computational cost.…”

Section: Streaming Approachesmentioning

confidence: 99%

Hyper-parameter Optimization for Latent Spaces

Veloso

Caroprese²,

König

et al. 2021

Machine Learning and Knowledge Discovery in Databases. Research Track

View full text Add to dashboard Cite

We present an online optimization method for time-evolving data streams that can automatically adapt the hyper-parameters of an embedding model. More specifically, we employ the Nelder-Mead algorithm, which uses a set of heuristics to produce and exploit several potentially good configurations, from which the best one is selected and deployed. This step is repeated whenever the distribution of the data is changing. We evaluate our approach on streams of real-world as well as synthetic data, where the latter is generated in such way that its characteristics change over time (concept drift). Overall, we achieve good performance in terms of accuracy compared to state-of-the-art AutoML techniques.

show abstract

“…Continuous data streaming for training should be supported by the adaptive classifier, which should be able to adjust to the changing behavior of the power system thanks to integrated renewables. This process is known as incremental or online learning [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

2023

View full text Add to dashboard Cite

Adaptive protection schemes have been developed to address the problem of behavior-changing power systems integrated with inverter-based generation (IBG). This paper proposes a machine-learning-based fault detection and classification technique using a setting-group-based adaptation approach. Multigroup settings were designed depending on the types of power generation (synchronous generator, PV plant, and type-3 wind farm) connected to a transmission line in the 39-Bus New England System. For each system topology, an optimized pretrained ensemble tree classifier was used. The adaptation process has two phases: an offline learning phase to tune the classifiers and select the optimum subset of features, and an online phase where the circuit breaker (CB) status and the active output power of the generators are continuously monitored to identify the current system topology and to select the appropriate setting group. The proposed system achieved an average accuracy of 99.4%, a 99.5% average precision, a 99.9% average specificity, and a 99.4% average sensitivity of classification. The robustness analysis was conducted by applying several fault scenarios not considered during training, which include different transmission network configurations and different penetration levels of IBGs. The case of incorrect selection of the appropriate setting group resulting from selecting the wrong topology is also considered. It was noticed that the performance of developed classifiers deteriorates when the transmission network is reconfigured and the incorrect setting group is selected.

show abstract

Adaptive SVM for Data Stream Classification

Cited by 7 publications

References 17 publications

Hyperparameter self-tuning for data streams

Hyperparameter self-tuning for data streams

Hyper-parameter Optimization for Latent Spaces

Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

Contact Info

Product

Resources

About