scholarly journals A Novel Ensemble Learning Approach for Corporate Financial Distress Forecasting in Fashion and Textiles Supply Chains

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Gang Xie ◽  
Yingxue Zhao ◽  
Mao Jiang ◽  
Ning Zhang
Keyword(s):  
Supply Chains ◽  
Ensemble Learning ◽  
Financial Distress ◽  
Back Propagation ◽  
Support Vector ◽  
Learning Approach ◽  
Chinese Listed Companies ◽  
Forecasting Performance ◽  
Forecasting Errors ◽  
Textile Sector

This paper proposes a novel ensemble learning approach based on logistic regression (LR) and artificial intelligence tool, that is, support vector machine (SVM) and back-propagation neural networks (BPNN), for corporate financial distress forecasting in fashion and textiles supply chains. Firstly, related concepts of LR, SVM, and BPNN are introduced. Then, the forecasting results by LR are introduced into the SVM and BPNN techniques which can recognize the forecasting errors in fitness by LR. Moreover, empirical analysis of Chinese listed companies in fashion and textile sector is implemented for the comparison of the methods, and some related issues are discussed. The results suggest that the proposed novel ensemble learning approach can achieve higher forecasting performance than those of individual models.

PREDICTING FINANCIAL DISTRESS OF CHINESE LISTED COMPANIES USING ROUGH SET THEORY AND SUPPORT VECTOR MACHINE

2011 ◽  
Vol 28 (01) ◽  
pp. 95-109 ◽  
Author(s):  
YU CAO ◽  
GUANGYU WAN ◽  
FUQIANG WANG
Keyword(s):  
Support Vector Machine ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Rough Set Theory ◽  
Listed Companies ◽  
Support Vector ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

Effectively predicting corporate financial distress is an important and challenging issue for companies. The research aims at predicting financial distress using the integrated model of rough set theory (RST) and support vector machine (SVM), in order to find a better early warning method and enhance the prediction accuracy. After several comparative experiments with the dataset of Chinese listed companies, rough set theory is proved to be an effective approach for reducing redundant information. Our results indicate that the SVM performs better than the BPNN when they are used for corporate financial distress prediction.

Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs

2013 ◽  
pp. 212-220
Author(s):  
Xiu Xin ◽  
Xiaoyi Xiong
Keyword(s):  
Financial Distress ◽  
Transfer Functions ◽  
Back Propagation ◽  
Principal Component ◽  
Listed Companies ◽  
Quantitative Prediction ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies ◽  
Base Functions

The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.

Hyperspectral Estimation of Apple Canopy Chlorophyll Content Using an Ensemble Learning Approach

2021 ◽  
Vol 37 (3) ◽  
pp. 505-511
Author(s):  
Xueyuan Bai ◽  
Yingqiang Song ◽  
Ruiyang Yu ◽  
Jingling Xiong ◽  
Yufeng Peng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Regression Model ◽  
Chlorophyll Content ◽  
Ensemble Learning ◽  
Vegetation Indices ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Learning Approach ◽  
Stress Monitoring ◽  
Apple Trees

HighlightsMonitored the canopy chlorophyll content of apple trees using hyperspectral reflectance information.Constructed support vector machine combination regression model (C-SVR) based on five-fold cross validation and support vector machine regression approach.Compared estimation accuracy of ensemble learning models (C-SVR, RF), machine learning models (SVR, ANN), and PLSR models for apple canopy chlorophyll content.Abstract. Rapidly and effective monitoring of the canopy chlorophyll content (CCC) of apple trees is of great significance for crop stress monitoring in precision agriculture. This study attempted to use hyperspectral vegetation indices (VIs) to estimate the CCC of apple trees based on ensemble learning approach. In this study, vegetation indices combined by any two wavelengths from 400 to 1100 nm were constructed to calculate the correlation coefficient with the CCC in apple. We constructed a partial least squares regression model (PLSR), artificial neural network regression model (ANN), support vector machine regression (SVR), random forest regression (RF) model and support vector machine combination regression model (C-SVR) based on combinations of VIs to improve the estimation accuracy in apple CCC. The results showed that the correlation coefficients between NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), DVI (572,532), and apple CCC were all above 0.76. The CCC estimation model using the RF and C-SVR approach constructed by the NDVI (949,695), OSAVI (828,705), RDVI (741,725), RVI (716,707), and DVI (572,532) achieved the better estimation results, and the R2V, RMSEV, and RPDV values of models were 0.76, 0.131(mg . g-1), 2.04 and 0.78, 0.127(mg . g-1), 2.12, respectively. Compared with the PLSR, ANN, and SVR model, the R2V and RPDV values of C-SVR model were increased by 4%, 1.2%, 3.8%, and 5.0%, 28.4%, 7.1%, respectively. The results show that using C-SVR approach to estimating the apple CCC can realize high accuracy of quantitative estimation. Ensemble learning approach is an effective method for monitoring the nutrient status of fruit trees based on hyperspectral technique. Keywords: Apple tree canopy, Chlorophyll content, Crop stress monitoring, Ensemble learning, Hyperspectral, Vegetation index.

Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs

2011 ◽  
Vol 3 (4) ◽  
pp. 6-14
Author(s):  
Xiu Xin ◽  
Xiaoyi Xiong
Keyword(s):  
Financial Distress ◽  
Transfer Functions ◽  
Back Propagation ◽  
Principal Component ◽  
Listed Companies ◽  
Quantitative Prediction ◽  
Back Propagation Algorithm ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Chinese Listed Companies

The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.

scholarly journals Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Marcin Fałdziński ◽  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Absolute Error ◽  
Futures Contracts ◽  
Support Vector ◽  
Garch Models ◽  
Forecasting Performance ◽  
Asymmetric Garch ◽  
Heating Oil ◽  
Forecasting Volatility ◽  
Forecasting Errors

We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

scholarly journals Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bingquan Liu ◽  
Yumeng Liu ◽  
Dong Huang
Keyword(s):  
Ensemble Learning ◽  
Nonuniform Distribution ◽  
Yeast Genome ◽  
Support Vector ◽  
Learning Approach ◽  
Dinucleotide Composition ◽  
Cross Covariance ◽  
Vector Machines ◽  
Genomic Regions ◽  
Auto Cross Covariance

Recombination presents a nonuniform distribution across the genome. Genomic regions that present relatively higher frequencies of recombination are called hotspots while those with relatively lower frequencies of recombination are recombination coldspots. Therefore, the identification of hotspots/coldspots could provide useful information for the study of the mechanism of recombination. In this study, a new computational predictor called SVM-EL was proposed to identify hotspots/coldspots across the yeast genome. It combined Support Vector Machines (SVMs) and Ensemble Learning (EL) based on three features including basic kmer (Kmer), dinucleotide-based auto-cross covariance (DACC), and pseudo dinucleotide composition (PseDNC). These features are able to incorporate the nucleic acid composition and their order information into the predictor. The proposed SVM-EL achieves an accuracy of 82.89% on a widely used benchmark dataset, which outperforms some related methods.

scholarly journals Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1452
Author(s):  
Jun Li ◽  
Yinghong Yu ◽  
Xinlin Qing
Keyword(s):  
Neural Network ◽  
Ensemble Learning ◽  
Composite Structures ◽  
Back Propagation ◽  
Support Vector ◽  
Time Of Arrival ◽  
Neural Network Ensemble ◽  
Monitoring Method ◽  
Reinforced Plastics ◽  
Fbg Sensors

Impact brings great threat to the composite structures that are extensively used in an aircraft. Therefore, it is necessary to develop an accurate and reliable impact monitoring method. In this paper, fiber Bragg grating (FBG) sensors are embedded in unidirectional carbon fiber reinforced plastics (CFRPs) during the manufacturing process to monitor the strain that is related to the elastic modulus and the state of resin. After that, an advanced impact identification model is proposed. Support vector regression (SVR) and a back propagation (BP) neural network are combined appropriately in this stacking-based ensemble learning model. Then, the model is trained and tested through hundreds of impacts, and the corresponding strain responses are recorded by the embedded FBG sensors. Finally, the performances of different models are compared, and the influence of the time of arrival (ToA) on the neural network is also explored. The results show that compared with a single neural network, ensemble learning has a better capability in impact identification.

Sign in / Sign up

Export Citation Format

Share Document