An approach towards missing data management using improved GRNN-SGTM ensemble method

Izonin, Ivan; Tkachenko, Roman; Verhun, Volodymyr; Zub, Khrystyna

doi:10.1016/j.jestch.2020.10.005

Cited by 48 publications

(31 citation statements)

References 42 publications

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“…For nonlinear input data, shallow machine learning methods obtain model parameters through training, such as support vector machines (SVMs) [ 21 ], the echo state network (ESO) [ 22 ], Boltzmann machines (BMs) [ 23 ], shallow artificial neural networks (ANNs) [ 24 ], generalized regression neural networks (GRNNs) [ 25 ], etc., which avoids the requirement of prior knowledge of the data. However, because of their simple structure, they cannot process large amounts of data.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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Section: Related Workmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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“…SVM revolves around employing high-dimensional feature spaces (built using transformational original variables) and the application of penalties to the resulting complexities by using a penalty term integrated within the error function [26]. Other approaches like the stacking-based General Regression Neural Network (GRNN) ensemble model are truly promising [27][28][29], but it has not been previously applied to DM1. In this sense, the authors of this work intend to analyze it in future research.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

et al. 2021

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Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

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“…However, the traditional identification methods of motor fault diagnosis based on a mathematical model and electrical signal are highly dependent on the accuracy of the model, and the selection of signal wave base has certain limitations, so the accuracy of motor fault feature extraction and analysis still needs to be improved. At present, with the continuous development of artificial intelligence, machine learning has been cross-applied in various fields [9], such as the recovery and prediction of missing data [10], the judgment of stock price changes [11], and the detection of urban road obstacles [12]. These areas span biology, medicine, machinery, finance, etc.…”

Section: Introductionmentioning

confidence: 99%

“…At present, with the continuous development of artificial intelligence, machine learning has been cross-applied in various fields [ 9 ], such as the recovery and prediction of missing data [ 10 ], the judgment of stock price changes [ 11 ], and the detection of urban road obstacles [ 12 ]. These areas span biology, medicine, machinery, finance, etc.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

Feng

Liu

et al. 2021

Entropy

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As a complex field-circuit coupling system comprised of electric, magnetic and thermal machines, the permanent magnet synchronous motor of the electric vehicle has various operating conditions and complicated condition environment. There are various forms of failure, and the signs of failure are crossed or overlapped. Randomness, secondary, concurrency and communication characteristics make it difficult to diagnose faults. Meanwhile, the common intelligent diagnosis methods have low accuracy, poor generalization ability and difficulty in processing high-dimensional data. This paper proposes a method of fault feature extraction for motor based on the principle of stacked denoising autoencoder (SDAE) combined with the support vector machine (SVM) classifier. First, the motor signals collected from the experiment were processed, and the input data were randomly damaged by adding noise. Furthermore, according to the experimental results, the network structure of stacked denoising autoencoder was constructed, the optimal learning rate, noise reduction coefficient and the other network parameters were set. Finally, the trained network was used to verify the test samples. Compared with the traditional fault extraction method and single autoencoder method, this method has the advantages of better accuracy, strong generalization ability and easy-to-deal-with high-dimensional data features.

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An approach towards missing data management using improved GRNN-SGTM ensemble method

Cited by 48 publications

References 42 publications

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Stacked Denoising Autoencoder

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