Data Stream Classification Based on Extreme Learning Machine: A Review

Xiu-lin, Zheng; Li, Peipei; Wu, Xindong

doi:10.1016/j.bdr.2022.100356

Cited by 12 publications

(6 citation statements)

References 138 publications

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“…One year later, a review of data-stream classification based on ELM was realized by Zheng et al [16]. The authors consider that little research has yet been conducted in this field.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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“…One year later, a review of data-stream classification based on ELM was realized by Zheng et al [16]. The authors consider that little research has yet been conducted in this field.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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“…This paper adopts a variety of economic indicators of counties and cities in Jilin Province to study and predict the development of rural revitalization from the perspective of the electric power industry, as well as to explore the application of electric power big data in rural revitalization, to provide scientific basis and decision-making support for the implementation of rural revitalization [1]. In addition, this paper also evaluates the development of rural revitalization through the analysis and prediction of rural electric power big data and its related economic indicators [2] and puts forward useful suggestions and inspiration, which is of great significance for promoting the process of rural revitalization, creating a beautiful countryside and achieving sustainable development.…”

Section: Introduction 11 Background and Significance Of The Studymentioning

confidence: 99%

Research on forecasting the development of counties and cities in Jilin province based on data mining algorithms

Chen,

Tian,

Wang

et al. 2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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With the continuous growth of China's national economy, the development of the countryside is facing unprecedented opportunities and challenges. This paper is based on Python language, under the big data of electric power, to study the development of rural revitalization and make predictions from the perspective of the electric power industry by combining more than twenty aspects such as Gross Domestic Product (GDP), enterprise income tax and rural electricity consumption of counties and cities in Jilin Province. Firstly, this paper collects economic data and electric power data of counties and cities, constructs multidimensional datasets, and applies data mining methods for data mining analysis and prediction model construction. Second, we use machine learning algorithms such as clustering and classification to analyze and predict the development trend of electric power big data. Finally, we assess the development of rural revitalization and provide useful suggestions and insights by analyzing and predicting electric power big data and its related indicators in counties and cities.

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“…The underlying characteristics of these dynamic message streams pose some serious challenges to effective classification, such as concept drift, concept evolution, latency and adversarial attacks [5,6]. First, the concepts embedded in a stream change over time.…”

Section: Introductionmentioning

confidence: 99%

Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions

Hojas-Mazo,

Maciá-Pérez,

Berná Martínez

et al. 2024

Algorithms

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Analysing message streams in a dynamic environment is challenging. Various methods and metrics are used to evaluate message classification solutions, but often fail to realistically simulate the actual environment. As a result, the evaluation can produce overly optimistic results, rendering current solution evaluations inadequate for real-world environments. This paper proposes a framework based on the simulation of real-world message streams to evaluate classification solutions. The framework consists of four modules: message stream simulation, processing, classification and evaluation. The simulation module uses techniques and queueing theory to replicate a real-world message stream. The processing module refines the input messages for optimal classification. The classification module categorises the generated message stream using existing solutions. The evaluation module evaluates the performance of the classification solutions by measuring accuracy, precision and recall. The framework can model different behaviours from different sources, such as different spammers with different attack strategies, press media or social network sources. Each profile generates a message stream that is combined into the main stream for greater realism. A spam detection case study is developed that demonstrates the implementation of the proposed framework and identifies latency and message body obfuscation as critical classification quality parameters.

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Data Stream Classification Based on Extreme Learning Machine: A Review

Cited by 12 publications

References 138 publications

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Research on forecasting the development of counties and cities in Jilin province based on data mining algorithms

Framework Based on Simulation of Real-World Message Streams to Evaluate Classification Solutions

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