Outlier Robust Extreme Machine Learning for multi-target regression

Silva, Bruno Légora Souza da; Inaba, Fernando Kentaro; Salles, Evandro Ottoni Teatini; Ciarelli, Patrick Marques

doi:10.1016/j.eswa.2019.112877

Cited by 33 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation ( 1) is the abovementioned calculation formula for averaging the spectrum value of the frequency point, where the number of inverters is N; here, it refers to 300 in the experiment; then, calculate the spectrum band by equation (2). e spectrum value corresponds to the frequency point in the upper limit of the range.…”

Section: Fault Diagnosis Methods Based On Fullmentioning

confidence: 99%

“…Insulated Gate Bipolar Transistor (IGBT) is a power semiconductor device consisting of BJT and MOS, which has been fully applied to high-precision fields, such as new energy power generation, wind power, electric locomotive traction, and electric vehicles [1,2]. In these applications, IGBT devices typically work in a harsh operating environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

[Retracted] Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

Chen

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.

show abstract

Section: Fault Diagnosis Methods Based On Fullmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

Chen

Zhang

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The idea of elastic net proposed by Zou and Hastie [36] and his partners, which performs well in the tasks that the pairwise correlations of variables are very high. Similar to 1 -RELM, 1 -2 -RELM has no analytical solutions, so some iterative algorithms [25], [43] are proposed for solving the model (8).…”

Section: B Regularized Elmmentioning

confidence: 99%

An Adaptive Learning Algorithm for Regularized Extreme Learning Machine

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

Extreme learning machine (ELM) has become popular in recent years, due to its robust approximation capacity and fast learning speed. It is common to add a 2 penalty term in basic ELM to avoid over-fitting. However, in 2 -regularized extreme learning machine ( 2 -RELM), choosing a suitable regularization factor is random and time consuming. In order to select a satisfactory regularization factor automatically, we proposed an adaptive regularized extreme learning machine (A-RELM) by replacing the regularization factor with a function. The function is defined in terms of the output weights named regularization function. And an iterative algorithm is proposed for obtaining the output weights, therefore, allowing for deriving their values simultaneously. Besides, the constructed regularization function ensures the convexity of the model, which contributes to a globally optimal solution. The convergence analysis of the iterative algorithm guarantees the effectiveness of the model training. Experimental results on some UCI benchmarks and the Yale face database B indicate the superiority of our proposed algorithm.

show abstract

“…At last, we have the function of information divergence (21). Substituting (17) into 21, we get function (22).…”

Section: Elm Under Minimum Information Divergence Criterionmentioning

confidence: 99%

“…Function fitting results of four algorithms upon Sinc with three noise: (a)α-stable noise (α = 1.5,τ = 0.5); (b)α-stable noise (α = 1.3,τ = 1); (c) Laplace noise (0,0.5)usually chosen by grid-search method and cross validation method, such as k-fold, as done by Inaba et al[20], Zhang et al[13], Huang et al[21], Da Silva et al[22] and others. Similarly, in this part, we obtain the best parameter combination by the grid search on each parameter and the five-fold cross-validation on every training set.…”

mentioning

confidence: 99%

Extreme Learning Machine Under Minimum Information Divergence Criterion

Song

Pan

Liu³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In recent years, extreme learning machine (ELM) and its improved algorithms have been successfully applied to various classification and regression tasks. In these algorithms, MSE criterion is commonly used to control training error. However, MSE criterion is not suitable to deal with outliers, which can exist in general regression or classification tasks. In this paper, a novel extreme learning machine under minimum information divergence criterion (ELM-MinID) is proposed to deal with the training set with noises. In minimum information divergence criterion, the Gaussian kernel function and Euclidean information divergence are utilized to substitute the mean square error (MSE) criterion to enhance the antinoise ability of ELM. Experimental results on two synthetic datasets and eleven benchmark datasets show that this method is superior to traditional ELMs.

show abstract

Outlier Robust Extreme Machine Learning for multi-target regression

Cited by 33 publications

References 24 publications

[Retracted] Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

[Retracted] Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

An Adaptive Learning Algorithm for Regularized Extreme Learning Machine

Extreme Learning Machine Under Minimum Information Divergence Criterion

Contact Info

Product

Resources

About