A weighted SVM ensemble predictor based on AdaBoost for blast furnace Ironmaking process

Si, Luo; Dai, Zian; Chen, Tianxin; Chen, Hongyi; Jian, Ling

doi:10.1007/s10489-020-01662-y

Cited by 19 publications

(10 citation statements)

References 33 publications

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“…Considering the idea that new samples and old samples should have different weights, Sun et al 30 proposed an error-time-based sample weight updating function in the AdaBoost iteration for dynamic financial distress prediction. Luo et al 31 introduced that the weight distribution of training samples is determined according to the performance of the component classifier. Other studies have been carried out on this issue.…”

Section: Introductionmentioning

confidence: 99%

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Sheng

Liu

Söffker

2022

Structural Health Monitoring

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Adaptive boosting (AdaBoost) algorithms fuse multiple weak classifiers to generate a strong classifier by adaptively determining the fusion weights of the weak classifiers. According to incorrect or correct classification results, sample weights become larger or smaller. However, this weight update scheme neglects valuable information in the results. Moreover, an important requirement for weak classifiers is an accuracy higher than random guessing. This requirement is likely to lead to an unexpected result. This means that several generated weak classifiers with similar classification results cannot learn from each other. Consequently, the advantage of fusing multiple weak classifiers disappears. The classification and therefore distinction of different failure modes in materials is a typical task for classical nondestructive testing approaches as well as for new approaches based on machine learning methods. In the case different approaches can be applied, the main question is, which and how tuned approaches provide the best results in terms of accuracy or similar metrics. In the multi-damage classification task distinguishing different physical failure mechanisms in Carbon Fiber Reinforced Polymer (CFRP), the above two aspects complicate the application of AdaBoost algorithms. To improve the results, a novel AdaBoost with distance-based weighted least square support vector machine (WLSSVM) and filter factor is proposed. The distance-based WLSSVM is employed to increase the diversity of weak classifiers, the distances of the classification plane and samples are used to measure the classification task. The filter factor is proposed to filter out unnecessary classifiers contributing less to the final decision. The experimental results demonstrate that the improved AdaBoost schemes with distance-based WLSSVM and filter factor outperform state-of-the-art algorithms. The effects of the scheme using the new weighted update and the filter factor on the algorithm are discussed, respectively. The experimental results show that the combination of the two schemes perform better than other schemes.

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Section: Introductionmentioning

confidence: 99%

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Sheng

Liu

Söffker

2022

Structural Health Monitoring

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“…Zhang uses distance to weight the k-nearest neighbor (KNN) to make it have good robustness and performance [23]. Luo used the AdaBoost algorithm to adjust the weight of SVM to relieve data imbalance [24]. A novel ensemble learning method based on multiple deep models, multi-objective optimization algorithm and designed selection strategy is proposed by Ma for intelligent fault diagnosis of rotor faults and rolling bearings [25].…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on similarity measure and ensemble learning

Wang¹,

Wang²,

Li³

2021

Meas. Sci. Technol.

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Ensemble learning aggregates the predictions of diverse predictors. Most existing ensemble learning methods gather the base classifiers based on the weight of accuracy. However, if the weight is adjusted only according to the correct rate of the base classifiers, it ignores the different types of errors caused by different working principles of the base classifiers. Blindly taking the correct rate of the basis classifier as the weight will increase the probability of the classifier making the same mistake and thus reduce the correct rate. To address this problem, this paper proposes a multi-round voting method based on similarity measurement. In the proposed method, the characteristic parameters of the data are extracted and PCA dimensionality reduction is used to train several base classifiers, which achieved high accuracy of about 90% at first. Then, we extract the confusion matrix of these base classifiers to facilitate the calculation of the similarity measurement matrix and the obtainment of the similarity matrix between base classifiers. Later, on the basis of the number of base classifiers and the values of the similarity matrix, we set a certain number of similar thresholds. Finally, according to the comparison of similarity value and similarity thresholds, a multi-round voting classifier is trained. By comparing the hard voting classifier, multi-round voting classifier and their base classifiers, the experimental results verify that the multi-round voting classifier has higher accuracy than other methods.

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“…Several researchers aimed to develop ML algorithms for classification problems, including the decision tree (DT), naive Bayes (NB), k-nearest-neighbor (K-NN), and support vector machine (SVM), etc. [1], [14]- [16]. DT (a graphic method of intuition using probability analysis [14]) is a decision analysis method used to calculate the probability that the expected value of the net present value is greater than or equal to zero.…”

Section: Introductionmentioning

confidence: 99%

“…The construction of the DT is a recursive process. On the other hand, SVM aims to find a separate hyperplane in the feature space, which divides different data instances into various labels to achieve classification [16]. This algorithm does not make any assumptions about the distribution of the original data set, so it is widely used in biomedical engineering, chemical materials, and physical spectra.…”

Section: Introductionmentioning

confidence: 99%

Method for Classifying a Noisy Raman Spectrum Based on a Wavelet Transform and a Deep Neural Network

et al. 2020

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Because it is relatively difficult in practice to classify the Raman spectrum under baseline noise and additive white Gaussian noise environments, this paper proposes a new framework based on a wavelet transform and deep neural network for identification of noisy Raman spectra. The framework consists of two main engines. Wavelet transform is proposed as the framework front end for transforming the 1-D noise Raman spectrum to two-dimensional data. The two-dimensional data are fed to the framework back end, which is a classifier. The optimum classifier is chosen by implementing several traditional machine learning (ML) and deep learning (DL) algorithms, and we investigate their classification accuracy and robustness performances. The four chosen MLs are naive Bayes (NB), a support vector machine (SVM), a random forest (RF) and a k-nearest neighbor (KNN), and a deep convolution neural network (DCNN) was chosen as a DL classifier. Noise-free, Gaussian noise, baseline noise, and mixed-noise Raman spectra were applied to train and validate the ML and DCNN models. The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectra (10-30 dB noise power). Based on the simulation, the accuracy of the DCNN classifier is 9% higher than that of the NB classifier, 3.5% higher than the RF classifier, 1% higher than the KNN classifier, and 0.5% higher than the SVM classifier. In terms of robustness to mixed noise scenarios, the framework with the DCNN back end showed superior performance compared with the other ML back ends. The DCNN back end achieved 90% accuracy at 3 dB SNR, while the NB, SVM, RF, and K-NN back ends required 27 dB, 22 dB, 27 dB, and 23 dB SNR, respectively. In addition, in the low-noise test dataset, the F-measure score of the DCNN back end exceeded 99.1%, and the F-measure scores of the other ML engines were below 98.7%.

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A weighted SVM ensemble predictor based on AdaBoost for blast furnace Ironmaking process

Cited by 19 publications

References 33 publications

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

A novel adaptive boosting algorithm with distance-based weighted least square support vector machine and filter factor for carbon fiber reinforced polymer multi-damage classification

Bearing fault diagnosis method based on similarity measure and ensemble learning

Method for Classifying a Noisy Raman Spectrum Based on a Wavelet Transform and a Deep Neural Network

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