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Evaluation of the forestry and environmental conservation policies in Western China with multi-output regression method

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 157卷 239-246页

作者： An, Xin Wen, Yali Zhang, Yaoqi Xu, Shuo Beijing Forestry Univ Sch Econ & Management Beijing Peoples R China Auburn Univ Sch Forestry & Wildlife Auburn AL 36849 USA Beijing Univ Technol Sch Econ & Management Beijing Peoples R China

Western China serves as a significant national ecological security barrier, but the eco-environment is deteriorating. The government of China has been making great efforts to reduce the loss of soil, improve water retention, and generally protect biodiversity and eco-systems in western China by implementing several large-scale forestry and environmental conservation policies (FECPs) from 1999. The primary objective of this study is to assess the potential efficacy of all the FECPs and understand which forest policies contribute more to developing social economy, protecting natural resource, and improving ecological environment in western China. Due to the underlying complicated relatedness amongst multiple policies in terms of social economy, natural resource and ecological environment, it disables many traditional regression approaches to be applicable to this case here. In this work, the evaluation of the multiple policies is regarded as a multi-output regression problem from input indicator system to implementation effect indicator system, and an evaluation framework is proposed. Finally, experimental results on the data from China Statistical Yearbook on Environment show that our evaluation framework is feasible and efficient.

关键词： Forestry and environmental conservation policies multi-output regression Implementation effect Western China

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Deep Learning for multi-output regression Using Gradient Boosting

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IEEE ACCESS 2024年 12卷 17760-17772页

作者： Emami, Seyedsaman Martinez-Munoz, Gonzalo Univ Autonoma Madrid UAM Escuela Politecn Super Madrid 28049 Spain

This paper presents a novel methodology to address multi-output regression problems through the incorporation of deep-neural networks and gradient boosting. The proposed approach involves the use of dense layers as additive models within the Gradient Boosting framework using an auto transfer learning technique. At each boosting iteration, the deep model is cloned with the already trained layers frozen, and a new dense layer is concatenated to the frozen ones. Subsequently, only the weights of the newly added layer are trained in order to reduce the complexity of the learning task. Each layer is trained on the residuals of the squared loss function from previous iterations, resulting in the creation of a robust sequentially deep-trained neural network ensemble. Our experimental results demonstrate that the proposed approach leads to a significant improvement in the performance of the deep framework, resulting in more accurate predictions and improved model interpretability.

关键词： Artificial neural networks Training multitasking Boosting Task analysis Predictive models Neurons Gradient methods Deep neural network gradient boosting multi-output regression

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Leverage triple relational structures via low-rank feature reduction for multi-output regression

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multiMEDIA TOOLS AND APPLICATIONS 2017年第16期76卷 17461-17477页

作者： Zhang, Shichao Yang, Lifeng Deng, Zhenyun Cheng, Debo Li, Yonggang Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China

multi-output regression aims at learning a mapping from feature variables to multiple output variables. It is significant to utilize variety of inherent relational structure information of observations to conduct multi-output regression task when learning a best mapping from high-dimensional data. In this paper, we propose a new multi-output regression method, which simultaneously takes advantage of the low-rank constraint, sample selection, and feature selection in a unified framework. We first take the effect of low-rank constraint to search the correlation of output variables and impose a"" (2,p) -norm regularization on the coefficient matrix to capture the correlation between features and outputs. And then, the a"" (2,p) -norm on the loss function is designed to discover the correlation between samples, so as to select those informative samples to learn the model for improving predictive capacity. Thirdly, orthogonal subspace learning is exploited to ensure multi-output variables share the same low-rank structure of data by rotating the results of feature selection. In addition, to get the optimal solution of the objective function, we propose an effective iterative optimization algorithm. Finally, we conduct sets of experimental results on real datasets, and show the proposed method outperforms the state-of-the-art methods in terms of aCC and aRMSE.

关键词： multi-output regression Low-rank regression Feature selection Orthogonal subspace learning

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Simultaneous quantification of Au and Ag composition from Au-Ag bi-metallic LIBS spectra combined with shallow neural network model for multi-output regression

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APPLIED PHYSICS B-LASERS AND OPTICS 2021年第9期127卷 1-11页

作者： Narlagiri, Lingamurthy Soma, Venugopal Rao Univ Hyderabad Adv Ctr Res High Energy Mat ACRHEM Hyderabad 500046 Telangana India

The multi-output regression model has been used in this work to predict the simultaneous compositions of gold (Au) and silver (Ag) from the laser-induced breakdown spectroscopy (LIBS) spectra of an Ag-Au bimetallic target. multi-output regression with shallow neural networks uses two nodes at the output layer to predict the composition of one element each. The LIBS spectra of the bimetallic target at three different compositions (Au20-Ag80, Au50-Ag50, and Au80-Ag20) in conjunction with aluminium, copper, and bronze LIBS spectra (substituting for Au0-Ag0 composition) were used to train the model. Principal component analysis (PCA) was performed for dimensionality reduction and the output of the PCA was fed to the regression model. The trained model, after evaluation on the test set, was used for predictions from unseen LIBS spectra of Au30-Ag70, which was not present in the original training set. We have demonstrated the success of this model with an error of < 10%. For the first time, to the best of our knowledge, we used the time-resolved spectra for data augmentation to generalize the model and likewise to study any further improvement in the performance of the model. This facilitated in generalizing the model, which in turn improved the performance of the model. This method can further be developed to predict the composition of multiple elements simultaneously. The terminology used in the deep learning models is elucidated with simplified equations and illustrations.

关键词： LIBS Time-resolved spectra PCA Neural network Data augmentation Alloy target multi-output regression

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multi-output regression for imbalanced data stream

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EXPERT SYSTEMS 2023年第10期40卷

作者： Peng, Tao Sellami, Sana Boucelma, Omar Chbeir, Richard Aix Marseille Univ Univ Toulon CNRS LIS Marseille France Univ Pau & Pays Adour LIUPPA E2S UPPA Anglet France

In this article, we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (multiple output regression for Streaming Time Series), an online ensemble regressors devoted to non-stationary and imbalanced data streams. MORSTS relies on several multiple output regressor submodels, adopts a cost sensitive weighting technique for dealing with imbalanced datasets, and handles overfitting by means of the K-fold cross validation. For assessment purposes, experiments have been conducted on known real datasets and compared with known base regression techniques.

关键词： data streams imbalanced dataset multi-output regression non-stationarity overfitting time series

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multi-output regression Algorithm-Based Non-Dominated Sorting Genetic Algorithm II Optimization for L-Shaped Twisted Tape Insertions in Circular Heat Exchange Tubes

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ENERGIES 2024年第4期17卷

作者： Li, Shijie Qian, Zuoqin Liu, Ji Wuhan Univ Technol Sch Energy & Power Engn Wuhan 430063 Peoples R China Wuhan Business Univ Sch Mech & Elect Engn Wuhan 430056 Peoples R China

In this study, an optimization method using various multi-output regression models as model proxies within the NSGA-II framework was applied to determine the geometric parameters (P, W, D) of L-shaped twisted tape inserts for achieving the optimal overall heat transfer performance in a circular heat exchange tube. Herein, 4 multi-output regression models, namely, MOLR, MOSVR, MOGPR, and BPNN, were selected as proxy models and trained on a dataset containing 74 groups of data. The training results indicated that the MOGPR model, balancing high accuracy and low error conditions, exhibited moderate training times among the four algorithms. BPNN showed a comparatively lower comprehensive training effect, obtaining training accuracy close to that of the MOGPR algorithm but with approximately twice the training time. The worst fitting performance was gained with the MOSVR algorithm. Due to its fitting performance, the MOSVR algorithm was excluded from the subsequent NSGA-II model proxy. Through multi-objective optimization with NSGA-II, the optimal structural dimensions for three sets of L-shaped twisted tape inserts were obtained to achieve the best overall heat transfer efficiency within the tube.

关键词： heat transfer enhancement twisted tape insert multi-output regression multi-objective optimization

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Transformer based multi-output regression Learning for Wastewater Treatment 33

Transformer based Multi-output Regression Learning for Waste...

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IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Huang, Zi-lan Zhang, Luo-yi Zhang, Yi Qian, Shu-wei Wang, Chong-jun Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781665408981

Wastewater treatment is an important link to solve the pollution of water resources and realize the recycling of water resources. Predicting effluent water quality by influent water quality is an important part of the actual production process. The existing forecasting methods have the problem of low accuracy and efficiency, and cannot be used well in the actual production process. To tackle the above issues, we propose a novel method to use the transformer model to fit the wastewater treatment process which improves the prediction of effluent water quality. Experiments on various datasets validate the effectiveness of our proposed method. In the worst case, the sample prediction accuracy rate of our model achieves as high as 96.8%. At the same time, our model training with thousands of real production data, the training time is not more than two minutes. As far as we know, we are the first to use the transformer model in wastewater treatment tasks and achieve fast and accurate predictions. In the future, we will apply our model in actual production to achieve accurate predictions in dynamic data update scenarios.

关键词： wastewater treatment transformer multi-output regression

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Very Low Resolution Face Reconstruction Based on multi-output regression

Very Low Resolution Face Reconstruction Based on Multi-Outpu...

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IEEE Workshop on Electronics, Computer and Applications (IWECA)

作者： Li, Zhihui Hou, Ying Liu, Haibo Li, Xiang Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)9781479945658

According to the reconstruction in the existing systems whose model is big and reconstruct speed is slow as the actual situation, this paper proposes a piece-division multiple output regression algorithm based on the Bayesian multiple adaptive regression splines (MARS) model to make the regression of very low resolution(VLR) face data. This paper studies the low resolution(LR) face image reconstruction, which has less data, using the regression method to reconstruction is the most appropriate way with small model, fast reconstruction speed and accurate results as its advantages. The experiments prove the reconstruction accuracy from error and identification accuracy two aspects in this paper.

关键词： Super-resolution face reconstruction MARS multi-output regression

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output kernel learning for multi-output regression 8

Output kernel learning for multi-output regression

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8th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC)

作者： Tang, Yanqin Pan, Zhisong Zhang, Yanyan Army Engn Univ PLA Inst Command Control Engn Nanjing 210007 Peoples R China

ISBN: (纸本)9781538682463

multi-output regression is the extension of single output learning, which requires one input sample to predict multiple real-valued output variables simultaneously. Previous studies have shown that multi-output learning yield to a better predictive performance, mostly because of the multivariate nature and the compound dependencies between the multiple feature/target variables. But these relationships are sometimes hard to detect directly, so it is a challenge to reveal the relationship between these input features and output variables. In order to improve the accuracy of the prediction, we propose a new kernel-based multi-output regression method to study the internal relationship between output variables, called KMOR. Our method is searched the framework between multiple functions and output into a reproducing kernel Hilbert space of vector-valued functions. It constructs input kernel by user-defined to represent the relationship between input characteristics and output kernel by auto-learned to represent the relationship between output variable, we also use an efficient alternating optimization to solve the objective function. We have done experiments on fifteen data sets from a variety of interesting application domains and use relative root mean squared error (RRMSE) as performance measurement, which show that this method can improve the accuracy of regression and has very fast convergence speed in comparison with state-of-the-art methods for multi-output regression.

关键词： multi-output regression output kernel RKHS data-dependent

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The Effectiveness of multi-Label Classification and multi-output regression in Social Trait Recognition

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SENSORS 2021年第12期21卷 4127页

作者： Farlessyost, Will Grant, Kelsey-Ryan Davis, Sara R. Feil-Seifer, David Hand, Emily M. Purdue Univ Agr & Biol Engn W Lafayette IN 47907 USA Ithaca Coll Comp Sci Ithaca NY 14850 USA Univ Nevada Comp Sci & Engn Reno NV 89557 USA

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches-multi-label classification (MLC) and multi-output regression (MOR)-for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

关键词： first impressions social traits multi-label classification multi-output regression machine learning

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