Multi-target regression via non-linear output structure learning

Arashloo, Shervin Rahimzadeh; Kittler, Josef

doi:10.1016/j.neucom.2021.12.048

“…For this new problem, Tsoumakas et al [36] proposed a method, Random Linear Target Combinations (RLTC) that creates new output variables as random linear combinations of k original output variables. The non-linear output structure approach has already developed as well [5]. Some works presented that the multi-output regression framework can be combined with support vector regression [19] having an advantage.…”

Section: Related Workmentioning

confidence: 99%

Greedy control group selection for multi-explanatory multi-output regression problem

Szűcs,

Németh,

Kiss

2024

Preprint

0

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The problem of multi-output learning involves the simultaneous prediction of multiple outputs based on given inputs. This paper focuses on addressing this challenge, assuming that we can only monitor a subset of variables. This resource constraint led to a definition of a new kind of problem, that we call Multi-Explanatory Multi-Output Regression (MEMOR) task. The goal of MEMOR is to select explanatory variables that minimize the prediction error for target variables. The central question pertains to the optimal choice of a given number of variables to maximize the goodness of the regression. We propose two greedy approaches for identifying good explanatory variables, along with a linear approximation as a baseline. To evaluate the performance of the proposed algorithms, we compared the resulting explanatory variables with the optimal set obtained through an exhaustive search. Our greedy algorithms surpass the linear method with better regression results, while they are faster than the exhausted method. Both the MEMOR problem and the methods developed for it are well-suited for multi-dimensional data analysis with resource constraints.

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“…For this new problem, Tsoumakas et al [34] proposed a method, Random Linear Target Combinations (RLTC) that creates new output variables as random linear combinations of k original output variables. The non-linear output structure approach has already developed as well [5]. Some works presented that the multi-target regression framework can be combined with support vector regression [18] having an advantage.…”

Section: Related Workmentioning

confidence: 99%

Multi-target and control group selection for multi-explanatory multi-output regression problem

Szűcs,

Németh,

Kiss

2024

Preprint

0

View full text Add to dashboard Cite

The problem of multi-output learning involves the simultaneous prediction of multiple outputs based on given inputs. This paper focuses on addressing this challenge, assuming that we can only monitor a subset of variables. This resource constraint led to a definition of a new kind of problem, that we call Multi-Explanatory Multi-Output Regression (MEMOR) task. The goal of MEMOR is to select explanatory variables that minimize the prediction error for target variables. The central question pertains to the optimal choice of a given number of variables to maximize the goodness of the regression. We propose two greedy approaches for identifying good explanatory variables, along with a linear approximation as a baseline. To evaluate the performance of the proposed algorithms, we compared the resulting explanatory variables with the optimal set obtained through an exhaustive search. Our greedy algorithms surpass the linear method with better regression results, while they are faster than the exhausted method. Both the MEMOR problem and the methods developed for it are well-suited for multi-dimensional data analysis with resource constraints.

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A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

Ribeiro

¹

,

Pilastri

²

,

Moura

³

et al. 2023

Neural Comput & Applic

6

0

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In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.

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Multi-target regression via non-linear output structure learning

Cited by 9 publications

References 32 publications

Greedy control group selection for multi-explanatory multi-output regression problem

Greedy control group selection for multi-explanatory multi-output regression problem

Multi-target and control group selection for multi-explanatory multi-output regression problem

A data-driven intelligent decision support system that combines predictive and prescriptive analytics for the design of new textile fabrics

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