Safe semi supervised multi-target regression (MTR-SAFER) for new targets learning

Syed, Farrukh Hasan; Tahir, Muhammad Atif

doi:10.1007/s11042-018-6367-9

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Chaudhuri et al integrated extreme learning machines to propose an optimized forecasting model, thereby realizing real-time accurate forecasting of products in supply chain management [19]. Wan realizes the risk prediction of the supply chain by proposing a risk prediction model related to the manufacturing industry and ensures the healthy development of enterprises [20]. Proposing a combined model, Jaipuria et al constructed a hybrid forecasting technology for supply chain demand, thereby ensuring inventory safety and sufficient order quantity in the replenishment cycle of goods [21].…”

Section: Related Workmentioning

confidence: 99%

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Huang,

Lian

2023

IJACSA

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With the continuous development of Internet technology, the scale of Internet data is increasing day by day, and business forecasting has become more and more important in corporate business decision-making. Therefore, to improve the accuracy of Multi Target Regression in the actual e-commerce supply chain forecasting, research through the method of constructing the labeling feature for each target is optimized, the Multi-Target Regression via Sparse Integration and Label-Specific Features algorithm is obtained, and the experimental analysis is carried out on the performance of the algorithm and the application effect in the actual e-commerce supply chain. The experimental results show that the average of Relative Root Mean Square Error value of the research algorithm and is the lowest in most datasets, with a minimum of 0.058 in the effect experiments of prediction and label-specific features; in the effect and flexibility experiments of sparse sets, the lowest average of Relative Root Mean Square Error value of the research algorithm was 0.058, and the average rank value was the smallest. In addition, the average of Relative Root Mean Square Error value of the research algorithm is the smallest under the target variable of Y2 in the Enb data, and its value is 0.075. In the actual e-commerce supply chain forecast, the research algorithm has the highest score of 0.097 points. Overall, research algorithm has a better forecasting effect and higher performance, and has better practicality in practice, and can play a better effect in actual e-commerce supply chain forecasting.

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Section: Related Workmentioning

confidence: 99%

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Huang,

Lian

2023

IJACSA

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“…It paves the way for utilizing considerable quantities of unlabeled data which is likely to be used in numerous cases in association with typically smaller sets of labeled data (Van Engelen & Hoos, 2020). Researchers have proposed a great number of methods for the purpose of SSL concerning regression problems (Levatić et al., 2015; Li et al., 2017; Syed & Tahir, 2018). The semi‐supervised algorithms have been utilized in various scientific disciplines, yet to the best of our knowledge, they have not been employed to predict forest tree attributes using LiDAR data.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advancements in Individual Tree Detection and Forest Structural Attributes Estimation From LiDAR Data: MSITD and SAFER Approaches

Fallah,

Aghighi,

Matkan

2024

Earth and Space Science

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Currently, the information on the structural attributes of forests, such as the diameter at breast height (DBH) and the aboveground biomass (AGB), is being used widely in various disciplines. In this study, we first proposed a novel tree detection algorithm called multi‐scale individual tree detection (MSITD) algorithm, which combines the strengths of raster‐based and point‐based approaches in order to detect individual trees from LiDAR data accurately. After tree detection, the DBH and AGB attributes were estimated using the ground control data and metrics extracted from LiDAR data, adopting the safe semi‐supervised regression (SAFER) algorithm specifically designed for addressing regression problems with limited sample data. The performances of these algorithms were evaluated within a 10‐fold nested cross‐validation approach, utilizing the LiDAR data available in the NEWFOR project. The evaluation of the obtained results revealed that both the MSITD algorithm and the SAFER algorithm demonstrate substantial superiority compared to the benchmark algorithms in tree detection, especially for the understory trees, and forest structural attributes estimation, respectively. On average, the MSITD algorithm exhibited a 13% better performance in terms of extraction rate and an 11% better performance in terms of matching rate compared to the benchmark individual tree detection algorithms. For forest structural attributes estimation, the SAFER algorithm provided superior predictions compared to the benchmark ML algorithms, with the average RMSE of 3.38 cm, MAE of 2.84 cm, and R2 of 0.59 for DBH and the average RMSE of 75.79 kg, MAE of 70.02 kg, and R2 of 0.56 for AGB.

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Feature selection for semi-supervised multi-target regression using genetic algorithm

et al. 2021

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Safe semi supervised multi-target regression (MTR-SAFER) for new targets learning

Cited by 8 publications

References 27 publications

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Research on the Application of Multi-Objective Algorithm Based on Tag Eigenvalues in e-Commerce Supply Chain Forecasting

Advancements in Individual Tree Detection and Forest Structural Attributes Estimation From LiDAR Data: MSITD and SAFER Approaches

Feature selection for semi-supervised multi-target regression using genetic algorithm

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