9 results
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2. Improve robustness of machine learning via efficient optimization and conformal prediction.
- Author
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Yan, Yan
- Subjects
OPTIMIZATION algorithms ,MACHINE learning ,FORECASTING ,ALGORITHMS ,DIAGNOSIS - Abstract
The advance of machine learning (ML) systems in real‐world scenarios usually expects safe deployment in high‐stake applications (e.g., medical diagnosis) for critical decision‐making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance‐regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end‐to‐end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample‐efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP‐based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk‐aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems.
- Author
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Priyadarshini, Ishaani
- Subjects
OPTIMIZATION algorithms ,BIOLOGICALLY inspired computing ,DEEP learning ,MACHINE learning ,METAHEURISTIC algorithms ,PROBLEM solving ,ALGORITHMS - Abstract
In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm's feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO's wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network.
- Author
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Ling, Le, Zhang, Xuejian, Hu, Xiaobing, Fu, Yucong, Yang, Dongming, Liang, Enpei, and Chen, Yi
- Subjects
ARTIFICIAL neural networks ,ELASTOHYDRODYNAMIC lubrication ,OPTIMIZATION algorithms ,SPRAYING ,METAL spraying ,SPRAY painting ,MACHINE learning ,ALGORITHMS - Abstract
In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model's prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
5. Powered stochastic optimization with hypergradient descent for large-scale learning systems.
- Author
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Yang, Zhuang and Li, Xiaotian
- Subjects
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OPTIMIZATION algorithms , *STOCHASTIC learning models , *MACHINE learning , *INSTRUCTIONAL systems , *ONLINE education , *ALGORITHMS - Abstract
Stochastic optimization (SO) algorithms based on the Powerball function, namely powered stochastic optimization (PoweredSO) algorithms, have been confirmed, effectively, and demonstrated great potential in the context of large-scale optimization and machine learning tasks. Nevertheless, the issue of how to determine the learning rate for PoweredSO is a challenge and still unsolved problem. In this paper, we propose a class of adaptive PoweredSO approaches that are efficient, scalable and robust. It takes advantage of the hypergradient descent (HD) technique to automatically acquire an online learning rate for PoweredSO-like methods. In the first part, we study the behavior of the canonical PoweredSO algorithm, the Powerball stochastic gradient descent (pbSGD) method, with HD. The existing PoweredSO algorithms also suffer from the high variance because they take the similar algorithmic framework to SO algorithms, arising from sampling tactics. Therefore, the second portion develops an adaptive powered variance-reduced optimization method via utilizing both variance-reduced technique and HD. Moreover, we present the convergence analysis of the proposed algorithms and explore their iteration complexity on non-convex cases. Numerical experiments are conducted on machine learning tasks, verifying the superior performance over modern SO algorithms. • We propose a type of adaptive powered stochastic optimization algorithms. • The theoretical analysis of the resulting algorithms is established. • Experiments on machine learning tasks verify the efficacy of the algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
6. Learning-based algorithm for physician scheduling for emergency departments under time-varying demand and patient return.
- Author
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Liu, Ran, Wang, Zixiang, and Wang, Chengkai
- Subjects
- *
EMERGENCY physicians , *MACHINE learning , *OPTIMIZATION algorithms , *HOSPITAL emergency services , *INTEGER programming , *ALGORITHMS - Abstract
Because of the COVID-19 pandemic, Chinese hospitals are increasing their efforts to control the number of patients to decrease cross-infection risks. In this paper, we investigate the flexible physician scheduling problem for emergency departments (EDs), considering the constraint of the maximum number of emergency patients in one time period. We model the ED service system as a time-varying queue with returns and formulate the physician scheduling problem as a mixed-integer programming model. To solve the scheduling problem effectively, we design a learning-based two-stage optimization algorithm. In the first stage, we solve the physician staffing problem, in which two effective acceleration strategies based on machine learning models are developed. In the second stage, we propose a branch-and-price algorithm to determine the physician scheduling plan. Numerical experiments based on real-life data show that the proposed two-stage algorithm can effectively solve our flexible physician scheduling problem, and the scheduling plan obtained by the proposed two-stage algorithm can significantly improve the physician schedule without involving more physicians. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures.
- Author
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Roy, Dilip Kumar, Hossain, Mohamed Anower, Haque, Mohamed Panjarul, Alataway, Abed, Dewidar, Ahmed Z., and Mattar, Mohamed A.
- Subjects
OPTIMIZATION algorithms ,MACHINE learning ,ALGORITHMS ,METEOROLOGICAL stations ,EVIDENCE gaps ,CROP growth - Abstract
This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum ( T m a x ) and minimum ( T m i n ) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead T m a x and T m i n forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for T m a x and T m i n across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both T m a x and T m i n forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. Researcher at Kanazawa University Describes Research in Cervical Cancer (A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer).
- Subjects
OPTIMIZATION algorithms ,FEATURE selection ,CERVICAL cancer ,RESEARCH personnel ,CANCER diagnosis - Abstract
A recent study conducted at Kanazawa University in Japan explores the use of machine learning algorithms for the diagnosis of cervical cancer. The researchers propose a novel feature selection strategy called the Binary Harris Hawks Optimization (BHHO) algorithm, which aims to reduce the number of features used in diagnosis while improving accuracy. The BHHO algorithm demonstrates better stability and accuracy compared to other common wrapper-based feature selection methods, not only on cervical cancer datasets but also on other disease datasets. This research contributes to the ongoing efforts to improve early diagnosis and treatment of cervical cancer, which is one of the leading causes of cancer-related deaths among females worldwide. [Extracted from the article]
- Published
- 2024
9. New Breast Cancer Data Have Been Reported by Investigators at School of Computing (Swarm Intelligent Metaheuristic Optimization Algorithms-based Artificial Neural Network Models for Breast Cancer Diagnosis: Emerging Trends, Challenges and...).
- Subjects
ARTIFICIAL neural networks ,METAHEURISTIC algorithms ,CANCER diagnosis ,BREAST cancer ,OPTIMIZATION algorithms - Abstract
A recent report from investigators at the School of Computing in Tamil Nadu, India, discusses the use of swarm intelligent metaheuristic optimization algorithms-based artificial neural network models for breast cancer diagnosis. The report emphasizes the importance of early detection and the use of optimization algorithms to improve accuracy and efficiency in predicting breast cancer. The researchers compare different approaches and highlight the significance of swarm intelligent algorithms-based optimized artificial neural network models. The report also discusses evaluation metrics and future research directions in this field. This research has been peer-reviewed and provides valuable insights into breast cancer diagnosis. [Extracted from the article]
- Published
- 2024
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