Robust on-line neural learning classifier system for data stream classification tasks

Sancho-Asensio, Andreu; Orriols-Puig, Albert; Golobardes, Elisabet

doi:10.1007/s00500-014-1233-9

Cited by 15 publications

(5 citation statements)

References 37 publications

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“…However, in order to use such a classifier, it needs to be adapted to process the data coming in the real-time mode [ 31 , 32 ]. Figure 2 shows the algorithm used in developing the ANN-classifier [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Koteleva

Korolev

Zhukovskiy

et al. 2021

Sensors

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This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

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Section: Methodsmentioning

confidence: 99%

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Koteleva

Korolev

Zhukovskiy

et al. 2021

Sensors

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“…Sancho‐Asensio et al 33 proposed a supervised neural constructivist system (SNCS) for mining data streams with concept drift. The SNCS classifier uses a population of multilayer perceptrons (MLP) with feed‐forward topology (i.e., the signal propagates from inputs towards the output layer).…”

Section: Related Workmentioning

confidence: 99%

“…VFDT 29 Decision trees Uses Hoeffding bound to decide on the number of instances needed to be seen to split nodes CVFDT 30 Decision trees Extends on VFDT to use a fixed window size to determine the age of nodes VFDR 12 Decision rules Similar to VFDT, uses Hoeffding bound to determine the number of instances seen before a rule is expanded eRules 13 Decision rules Uses a sliding window to learn rules using the Prism algorithm G-eRules 31 Decision rules Extends eRules by using a Gaussian distribution to efficiently sample continuous attributes' values DELM 32 Neural network Uses two hidden layers to dynamically adjust the learning layer when concept drift is detected Online GA 19 Decision rules Uses online genetic algorithm that creates rules of each class in parallel GP 20 Decision rules Uses genetic programming with a sampling procedure for data stream classification under limited label budgets SNCS 33…”

Section: Algorithmmentioning

confidence: 99%

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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“…However there are some works where neural networks are used for data stream tasks [14], where an online perceptron is used to classify nonstationary imbalance data streams. They have also been used as base learners for ensemble methods [15,16].…”

Section: Introductionmentioning

confidence: 99%

Data Stream Classification Based on the Gamma Classifier

Uriarte-Arcia

López-Yáñez

Yáñez-Márquez

et al. 2015

Mathematical Problems in Engineering

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The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier) implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

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Robust on-line neural learning classifier system for data stream classification tasks

Cited by 15 publications

References 37 publications

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Data stream classification with ant colony optimisation

Data Stream Classification Based on the Gamma Classifier

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