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27th International Conference on Discovery Science

作者： Thies, Santo M. A. R. Alfaro, Juan C. Bengs, Viktor Ludwig Maximilians Univ Munchen Inst Informat Chair Artificial Intelligence & Machine Learning D-80799 Munich Germany Munich Ctr Machine Learning Munich Germany Univ Castilla La Mancha Lab Sistemas Inteligentes & Mineria Datos Dept Sistemas Informat Inst Invest Informat Albacete Albacete 02071 Spain

ISBN: (纸本)9783031789762;9783031789779

The partial label ranking (PLR) problem is a supervised learning scenario where the learner predicts a ranking with ties of the labels for a given input instance. It generalizes the well-known label ranking (LR) problem, which only allows for strict rankings. So far, previous learning approaches for PLR have primarily adapted LR methods to accommodate ties in predictions. This paper proposes using multi-output regression (MOR) to address the PLR problem by treating ranking positions as multivariate targets, an approach that has received little attention in both LR and PLR. To effectively employ this approach, we introduce several post-hoc layers that convert MOR results into a ranking, potentially including ties. This framework produces a range of learning approaches, which we demonstrate in experimental evaluations to be competitive with the current state-of-the-art PLR methods.

关键词： Preference learning Bucket order multi-output regression (Partial) label ranking

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Semantics-guided multi-task genetic programming for multi-output regression

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PATTERN RECOGNITION 2025年 161卷

作者： Wang, Chunyu Chen, Qi Xue, Bing Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

multi-output regression entails the simultaneous prediction of two or more output variables, presenting greater complexities than single-output regression due to the frequent interdependent relationships of these variables. Such dependencies mean that accurately predicting one variable typically requires careful analysis of its relationships with others. In this paper, multi-output regression problems are treated as multi-task problems, with a prediction of one output variable as a distinct task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method incorporates a semantics based crossover operator to identify the most informative subtree from a similar task that facilitates positive knowledge transfer. Empirical results indicate that our method significantly improves the training and testing performances of other multi-task GP methods, surpassing standard GP and GP with regressor chain on most examined regression datasets. Further analysis reveals that our proposed method can generate high-quality solutions by knowledge transfer and efficiently evolves similar GP models for analogous output variables, significantly enhancing positive knowledge transfer.

关键词： multi-output regression Genetic programming Evolutionary multi-task optimization

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CNN-based multi-output regression model to estimate infrastructural surface crack dimensions adopting a generalised patch size and FWHM-based width quantification

Digital Twins and Applications

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Digital Twins and Applications 2025年第1期2卷 75-102页

作者： Sudipta Debroy Arjun Sil Department of Civil Engineering National Institute of TechnologySilcharAssamIndia

To cater the need for real-time crack monitoring of infrastructural facilities,a CNN-regression model is proposed to directly estimate the crack properties from *** crack images and their corresponding masks obtained from a public dataset are cropped into patches of 256 square pixels that are classified with a pre-trained deep convolution neural network,the true positives are segmented,and crack properties are extracted using two different *** first method is primarily based on active contour models and level-set segmentation and the second method consists of the domain adaptation of a mathematical morphology-based method known as FIL-FINDER.A statistical test has been performed for the comparison of the stated methods and a database prepared with the more suitable *** advanced convolution neural network-based multi-output regression model has been proposed which was trained with the prepared database and validated with the held-out dataset for the prediction of crack-length,crack-width,and width-uncertainty directly from input image *** pro-posed model has been tested on crack patches collected from different *** loss has been used to ensure the robustness of the proposed model selected from a set of 288 different variations of ***,an ablation study has been conducted on the top 3 models that demonstrated the influence of each network component on the pre-diction ***,the best performing model HHc-X among the top 3 has been proposed that predicted crack properties which are in close agreement to the ground truths in the test data.

关键词： ablation CNN convolution neural network crack crack patch estimation FWHM length multi-output regression segmentation uncertainty width

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multi-output regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition 12

Multi-Output Regression for Integrated Prediction of Valence...

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12th International Winter Conference on Brain-Computer Interface (BCI)

作者： Choi, HyoSeon Woo, ChaeEun Kong, JiYun Kim, Byung Hyung Inha Univ Dept Elect & Comp Engn Incheon South Korea Inha Univ Dept Comp Engn Incheon South Korea Inha Univ Dept Artificial Intelligence Incheon South Korea

ISBN: (纸本)9798350309430

Previous research in electroencephalogram (EEG)based emotion recognition has assumed valence and arousal as independent dimensions and derived results using separate classification models for each. Our study aims to introduce multi-output regression to continuously predict the correlated valence and arousal. We compared the prediction performance of a total of three models: Single-output regression, multi-output regression, and multi-output regression with a Chain structure. The analysis yielded the following results: 1) The performance of multi-output and single-output was similar, but the multi-output method is more useful as it can produce multiple outputs at once. 2) Predictions and actuals for valence and arousal were similar. Furthermore, considering that human emotions are not constant during certain intervals, it can provide insights close to the ground truth for understanding changes in emotional states.

关键词： multi-output regression Emotion Recognition EEG Valence Arousal

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multi-output regression with Tag Correlation Analysis for Effective Image Tagging

Multi-Output Regression with Tag Correlation Analysis for Ef...

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19th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Cai, Hongyun Huang, Zi Zhu, Xiaofeng Zhang, Qing Li, Xuefei Univ Queensland St Lucia Qld 4072 Australia Guangxi Normal Univ Guilin 541004 Peoples R China Australia E Hlth Res Ctr Brisbane Qld 4006 Australia

ISBN: (纸本)9783319058139;9783319058122

Automatic image tagging is one of the most important research topics in multimedia. How to achieve accurate image tagging to bridge the semantic gap between images' content and users' semantic understanding has been widely studied in the last decade. One common approach is to convert image tagging to a multi-task learning problem. However, most existing methods ignore tag correlations in the learning process. In this paper, we show the importance of tag correlations in conducting multi-task learning. We formulate image tagging as a multi-output regression problem accounting for tag correlations, which are captured by the covariance matrix of the regression coefficients and the noise across all tags respectively. The combination of multi-output regression with tag correlation analysis takes advantage of the latent dependencies among tags to overcome limitations of existing work. Extensive experiments have been conducted on two benchmark datasets, and the results confirm that our approach outperforms the state-of-the-art methods.

关键词： Image tagging multi-output regression correlation analysis

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A Kaizen Programming algorithm for multi-output regression based on a heterogeneous island model

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NEURAL COMPUTING & APPLICATIONS 2023年第13期35卷 9299-9317页

作者： Ferreira, Jimena Torres, Ana Ines Pedemonte, Martin Univ Republica Fac Ingn Inst Comp INCO Herrera Y Reissig 585 Montevideo 11300 Uruguay Univ Republica Fac Ingn Inst Ingn Quim IIQ Herrera Y Reissig 585 Montevideo 11300 Uruguay Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA

This article proposes an algorithm to construct multi-output symbolic regression models in a single execution. The algorithm extends the single-output Kaizen programming (KP) to a multi-output KP. KP is a hybrid evolutionary algorithm used to solve symbolic regression problems, without making any prior assumptions on the structure of the models. The extension to multi-output KP is made through an island model (MOKPIM). The idea behind MOKPIM is to find common terms among the outputs by balancing the solution obtained by each island working independently on a different output, with their cooperation due to the periodic exchange of migrants. In a previous effort, we followed a different approach for extending KP to multi-output scenarios based on using a multi-output linear regression in the linear regression step of the algorithm (MOKPMLR). A comparative analysis of the performance of MOKPIM with the classical single-output KP, our previous multi-output approach for KP, a multi-output Gaussian Process, and a multi-output decision tree regressor was conducted. The evaluation of algorithms used four different schemes of term sharing;five classical benchmark functions and a chemical process case study were considered for each scheme. The numerical results show that MOKPIM is the best performing algorithm regarding both the independent analysis of each output and the global analysis of all the outputs together. The proposed algorithm MOKPIM outperformed the other multi-output symbolic regression methods tested in this work. It also obtained competitive results with state-of-the-art methods when the outputs were considered independently.

关键词： Evolutionary algorithms Data-driven models multi-output regression multi-output symbolic regression Machine learning

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A novel non-profiled side channel attack based on multi-output regression neural network

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JOURNAL OF CRYPTOGRAPHIC ENGINEERING 2024年第3期14卷 427-439页

作者： Do, Ngoc-Tuan Hoang, Van-Phuc Doan, Van Sang Quy Don Tech Univ Inst Syst Integrat 236 Hoang Quoc Viet Str Hanoi Vietnam Naval Acad Nha Trang Khanh Hoa Vietnam

Differential deep learning analysis (DDLA) is the first side-channel attack (SCA) evaluation using deep learning (DL) in the non-profiled scenario. However, DDLA requires many training processes to distinguish the correct key. In this paper, we propose an SCA evaluation technique using the multi-output regression neural network, which can simultaneously estimate all key hypotheses in a single training process. Consequently, the performance of non-profiled DL-based SCA can improve significantly. Concretely, two multi-output regression models based on multi-layer perceptron (MOR-MLP) and convolutional neural network (MOR-CNN) are introduced against various SCA protected schemes, such as masking, noise generation, and trace de-synchronization countermeasures. Especially, we first suggest using identity labeling for multi-output regression to determine the trend of the training metric for each hypothesis key in the non-profiled SCA scenario. As a result, the correct key can be distinguished easily. The proposed network is fine-tuned with different variants of the shared layer based on both power consumption and electromagnetic radiation data. The experimental results show that our proposed models using identity label work well on different SCA datasets. Significantly, our approach remarkably outperforms the DDLA model in terms of execution time and success rate. Specifically, the results of evaluating the de-synchronized dataset show that the MOR-CNN model performs the attacks up to 40 times faster than DDLACNN. Regarding masking dataset, the MOR-MLP model achieves a higher success rate of at least 30% and 44% in the case of using the fastest DDLAMLP model and the original DDLAMLP model, respectively.

关键词： Side channel attack Non-profiled attacks Deep learning multi-output regression

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Low-rank hypergraph feature selection for multi-output regression

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2019年第2期22卷 517-531页

作者： Zhu, Xiaofeng Hu, Rongyao Lei, Cong Thung, Kim Han Zheng, Wei Wang, Can Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China Univ North Carolina BRIC Ctr Chapel Hill NC 27599 USA Griffith Univ Sch Informat & Commun Technol Gold Coast Campus Southport Qld 4222 Australia

Current multi-output regression method usually ignores the relationship among response variables, and thus it is challenging to obtain an effective coefficient matrix for predicting the response variables with the features. We address these problems by proposing a novel multi-output regression method, which combines sparse feature selection and low-rank linear regression in a unified framework. Specifically, we first utilize a hypergraph Laplacian regularization term to preserve the high-order structure among all the samples, and then use a low-rank constraint to respectively discover the hidden structure among the response variables and explore the relationship among different features in a least square regression framework. As a result, we integrate subspace learning with sparse feature selection to select useful features for multi-output regression. We tested our proposed method using several public data sets, and the experimental results showed that our method outperformed other comparison methods.

关键词： Low-rank Subspace learning Sparse feature selection multi-output regression

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Regularization-based model tree for multi-output regression

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INFORMATION SCIENCES 2020年 507卷 240-255页

作者： Jeong, Jun-Yong Kang, Ju-Seok Jun, Chi-Hyuck Pohang Univ Sci & Technol Ind Management & Engn Pohang 37673 South Korea POSCO Tech Res Labs Res Project Dept Pohang 37859 South Korea

multi-output regression refers to the simultaneous prediction of several real-valued output variables to improve generalization performance by exploiting output relatedness. We propose a multi-output model tree that utilizes a regularization-based method to exploit the output relatedness when estimating linear models at leaf nodes. The proposed method can explain nonlinear input-output relation and provides easy interpretation of its mechanism based on input space partitioning and models at leaf nodes. The models exploit output relatedness by selecting common input variables to explain related output variables. We also present a computationally efficient two-stage splitting procedure that decreases the number of model estimations by analyzing residuals. We verify the effectiveness of the proposed method in a simulation study and demonstrate that it outperforms existing methods on several benchmark datasets. Furthermore, we apply the proposed method to real industry data as a case study to predict tensile qualities of plates. (C) 2019 Elsevier Inc. All rights reserved.

关键词： multi-output regression multi-target regression Model trees Sparse representation

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Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes

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International Journal of Sediment Research 2023年第6期38卷 859-870页

作者： Mir Jafar Sadegh Safari Shervin Rahimzadeh Arashloo Department of Civil Engineering Yasar University Department of Computer Engineering Bilkent University

The existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear *** the current study,incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning(RLRL) multi-output regression ***,the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional *** efficiency of the developed method is examined and compared against the available conventional regression *** experimental results indicate that the RLRL model yields better results than its *** particular,while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections,the proposed model provides satisfactory results for all channel *** better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.

关键词： Low-rank learning multi-output regression Sediment transport Sewer flow Shear stress approach Velocity approach

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