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Multi-objective optimization in high-dimensional patent layout using deep Bayesian network and hybrid algorithm

DISCOVER COMPUTING 2025年第1期28卷 1-22页

作者： Wu, Minghua Jiang, Wenwu Weixian Enterprise Management Consulting Co Ltd Patent Technol Anal Dept Anhui Hefei 230029 Anhui Peoples R China Univ Sci & Technol China Sch Humanities & Social Sci Hefei 230031 Anhui Peoples R China

This study introduces a novel multi-objective optimization framework (DBNO) that integrates deep Bayesian networks with a hybrid algorithm combining random search and innovation diffusion to address high-dimensional patent layout optimization challenges. The framework was developed in response to the increasing complexity of patent layout decisions, where traditional single-objective optimization methods prove inadequate for simultaneously addressing multiple conflicting objectives such as profit, risk, and sustainability. To evaluate the framework's effectiveness, we conducted comprehensive experiments comparing DBNO against established algorithms including genetic algorithm (GA), particle swarm optimization (PSO), and traditional Bayesian optimization methods. Performance metrics encompassed convergence speed, computational efficiency, optimization stability, and solution quality across multiple objectives. The results demonstrate that DBNO consistently outperforms benchmark algorithms, particularly in optimizing sustainability objectives. Notably, DBNO exhibited superior stability and higher success rates in the optimization process compared to GA and PSO, highlighting its robustness in handling complex high-dimensional optimization problems. Furthermore, the integration of innovation diffusion mechanisms significantly enhanced both the efficiency and accuracy of the optimization process. The primary contribution of this research lies in the novel combination of deep Bayesian networks with ensemble random search techniques, resulting in a powerful multi-objective optimization framework. This approach provides an effective solution for high-dimensional patent layout problems while offering new perspectives for patent strategic decision-making. The findings advance the field of multi-objective optimization and establish a foundation for future research in patent portfolio optimization.

关键词： Deep Bayesian network Multi-objective optimization Integrated random search Innovation diffusion Patent layout algorithm comparison Convergence speed Computational efficiency

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A Comparative Study of Predictive Analysis Using Machine Learning Techniques: Performance Evaluation of Manual and AutoML algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 12-31页

作者： Rezaul, Karim Mohammed Jewel, Md. Sudhan, Anjali Khan, Mifta Uddin Fernando, Maharage Roshika Sathsarani Siddiquee, Kazy Noor e Alam Jannat, Tajnuva Rahman, Muhammad Azizur Islam, Md Shabiul Wrexham Univ Fac Arts Sci & Technol Wrexham LL11 2AW Wales Ctr Appl Res Software & IT CARSIT 80a Ashfield St London E1 2BJ England Multimedia Univ Fac Engn FOE Cyberjaya 63100 Malaysia Cardiff Metropolitan Univ Dept Comp Sci Llandaff CampusWestern Ave Cardiff CF5 2YB Wales

In this study, we have compared manual machine learning with automated machine learning (AutoML) to see which performs better in predictive analysis. Using data from past football matches, we tested a range of algorithms to forecast game outcomes. By exploring the data, we discovered patterns and team correlations, then cleaned and prepped the data to ensure the models had the best possible inputs. Our findings show that AutoML, especially when using logistic regression can outperform manual methods in prediction accuracy. The big advantage of AutoML is that it automates the tricky parts, like data cleaning, feature selection, and tuning model parameters, saving time and effort compared to manual approaches, which require more expertise to achieve similar results. This research highlights how AutoML can make predictive analysis easier and more accurate, providing useful insights for many fields. Future work could explore using different data types and applying these techniques to other areas to show how adaptable and powerful machine learning can be.

关键词： Machine learning predictive analytics sports forecasting automated machine learning (AutoML) feature engineering model evaluation data pre-processing algorithm comparison football analytics sports betting team performance metrics exploratory data analysis (EDA) cross-validation techniques

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IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics

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EVOLUTIONARY COMPUTATION 2024年第3期32卷 205-210页

作者： de Nobel, Jacob Ye, Furong Vermetten, Diederick Wang, Hao Doerr, Carola Back, Thomas Leiden Univ LIACS Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler *** provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

关键词： Iterative optimization heuristics benchmarking algorithm comparison

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Modelling nitrous oxide emissions: comparing algorithms in six widely used agro-ecological models

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SOIL RESEARCH 2023年第6期61卷 523-541页

作者： Xing, Hongtao Smith, Chris. J. Wang, Enli Macdonald, Ben Warlind, David CSIRO Agr GPOB 1666 Canberra ACT 2601 Australia Queensland Govt Dept Environm & Sci Brisbane Qld 4001 Australia Lund Univ Inst Phys Geog & Ecosyst Sci Lund Sweden

Agricultural soils are the most important anthropogenic source of nitrous oxide (N2O) emissions. This occurs via two main pathways: (1) from microbial-mediated oxidation of ammonium to nitrite and nitrate;and (2) denitrification. Most agro-ecological models explicitly deal with these two pathways albeit with different degrees of process understanding and empiricism. Models that integrate the impact of multiple environmental factors on N2O emissions can provide estimates of N2O fluxes from complex agricultural systems. However, uncertainties in model predictions arise from differences in the algorithms, imperfect quantification of the nitrification and denitrification response to edaphic conditions, and the spatial and temporal variability of N2O fluxes resulting from variable soil conditions. This study compared N2O responses to environmental factors in six agro-ecological models. The comparisons showed that environmental factors impact nitrification and denitrification differently in each model. Reasons include the inability to apportion the total N2O flux to the specific N transformation rates used to validate and calibrate the simplifications represented in the model algorithms, and incomplete understanding of the multiple interactions between processes and modifying factors as these are generally not quantified in field experiments. Rather, N2O flux data is reported as total or net N2O emissions without attributing emissions to gross and/or net rates for specific N processes, or considering changes that occur between production and emissions. Additional measurements that quantify all processes understand the multiple interactions that affect N2O emissions are needed to improve model algorithms and reduce the error associated with predicted emissions.

关键词： agriculture agro-ecological models algorithm comparison denitrification N2O emissions nitrification soil soil-atmosphere flux

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Online Recognition of Small Vegetable Seed Sowing Based on Machine Vision

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IEEE ACCESS 2023年 11卷 134331-134339页

作者： Zhang, Weipeng Zhao, Bo Gao, Shengbo Ji, Yuxi Zhou, Liming Niu, Kang Qiu, Zhaomei Jin, Xin Chinese Acad Agr Mechanizat Sci Natl Key Lab Agr Equipment Technol Beijing 100083 Peoples R China Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471003 Henan Peoples R China

The lightweight, small diameter, and irregular shape of small vegetable seeds create difficulties for online monitoring of sowing quality. We propose a machine vision-based online monitoring method with a sowing test bench designed to address the challenges. Vision devices and image processing systems are employed to detect the quality of seed sowing. Firstly, the seed segmentation image is obtained by completing the steps of median filtering, graying and image segmentation. We then implement the Circumscribed circle method to detect the position of the seed. Afterward, the coordinate system is converted using calibrated results to eliminate non-seed impurities. Finally, we count the number of identified seeds to evaluate the recognition accuracy. The trial compared three algorithms: the image segmentation algorithm OTSU, the critical point localization algorithm SIFT, and the algorithm designed in the experiment. The algorithm we designed outperformed the others regarding recognition accuracy and processing time. The experimental method employed in the study encompasses various functionalities, including seeding counting, understanding detection, replaying, and monitoring deviations from seed bands during sowing. Cabbage seeds (1.50 mm-2.00 mm), tomato seeds (1.00 mm-1.50 mm), and radish seeds (0.50 mm-1.00 mm) were selected as the experimental subjects due to the uniform particle size distribution. The results demonstrate that the relative error between the online image recognition algorithm and the system's seeding rate monitoring is below 3.0%. Moreover, the accuracy of missed seeding monitoring is 92.5%, while the accuracy of deviation monitoring during seeding is 92.0%. We observed that the image recognition algorithm employed in the system achieved a processing time of 0.29 seconds, with a seed band recognition rate of 96.8%, fulfilling the monitoring requirements for small seed sowing experiments. The processed images and collected data are presented in real

关键词： Small vegetable seeds image recognition monitoring of sowing quality algorithm comparison

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A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14275-14378页

作者： Turgut, Oguz Emrah Turgut, Mert Sinan Kirtepe, Erhan Izmir Bakircay Univ Fac Engn & Architecture Dept Ind Engn Menemen Izmir Turkiye Ege Univ Fac Engn Dept Mech Engn Bornova Izmir Turkiye Sirnak Univ Dept Motor Vehicles & Transportat Technol Sirnak Turkiye

The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints.

关键词： algorithm comparison algorithm scalability Metaheuristic algorithms Real-world design problems

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Statistical comparisons of Classifiers by Generalized Stochastic Dominance

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-37页

作者： Jansen, Christoph Nalenz, Malte Schollmeyer, Georg Augustin, Thomas Ludwig Maximilians Univ Munchen Dept Stat Ludwigstr 33 D-80539 Munich Germany

Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect to several criteria. Every comparison framework is confronted with (at least) three fundamental challenges: the multiplicity of quality criteria, the multiplicity of data sets and the randomness of the selection of data sets. In this paper, we add a fresh view to the vivid debate by adopting recent developments in decision theory. Based on so-called preference systems, our framework ranks classifiers by a generalized concept of stochastic dominance, which powerfully circumvents the cumbersome, and often even self-contradictory, reliance on aggregates. Moreover, we show that generalized stochastic dominance can be operationalized by solving easy-to-handle linear programs and moreover statistically tested employing an multiple quality criteria simultaneously. We illustrate and investigate our framework in a simulation study and with a set of standard benchmark data sets.

关键词： algorithm comparison statistical test generalized stochastic dominance preference system decision theory

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Comparative Study Between Machine Learning algorithms Applied to Support QA Contracting Processes

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ENGINEERING LETTERS 2024年第8期32卷 1690页

作者： Velandia, David Santiago Bejarano Rodriguez, Jorge Enrique Rodriguez Lizarazo, David Stevens Gonzalez Univ Distrital Francisco Jose Caldas Telemat Engn Bogota Colombia

This study comprehensively compares different machine learning algorithms to assess their applicability in Quality Assurance (QA) contracting procedures. The evaluated algorithms encompass Nearest Neighbors, Linear SVM, Radial SVM, Gaussian Process, Decision Tree, Neural Network, Logistic Regression, Naive Bayes, and QDA. Following the Knowledge Discovery Database (KDD) process, the methodology includes a diverse set of evaluation metrics such as F1-Score, recall, accuracy, and AUC-ROC, as well as learning curves, boundary maps, confusion matrices, Matthews Correlation Coefficient (MCC), and complexity curves. According to the Gartner Magic Quadrant assessment, the results suggest that Neural Network, QDA, and Gaussian Process models exhibit strong performance and thorough evaluation, making them optimal for the case study presented in the paper. In contrast, Nearest Neighbors and Linear SVM models are considered suboptimal, indicating an opportunity to explore the reasons behind their behavior in the case study and how to modify them for improved results. The other algorithms also present various possibilities for adaptation to the current case study, either as models with limited analysis or as imprecise models that can offer valuable insights for future work on optimizing them more effectively. This study significantly contributes to advancing machine learning applications in recruitment procedures.

关键词： Machine Learning Quality Assurance hiring processes algorithm comparison bias reduction

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Constructing Bayesian optimal designs for discrete choice experiments by simulated annealing

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JOURNAL OF CHOICE MODELLING 2025年 55卷

作者： Mao, Yicheng Kessels, Roselinde van der Zanden, Tom Maastricht Univ Dept Data Analyt & Digitalizat POB 616 NL-6200 MD Maastricht Netherlands Univ Antwerp Dept Econ City CampusPrinsstr 13 B-2000 Antwerp Belgium

Discrete choice experiments (DCEs) investigate the attributes that influence individuals' choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that utilize existing information about the attributes' preferences. Given the nonlinear nature of choice models, the construction of an appropriate design requires efficient algorithms. Among these, the coordinate-exchange (CE) algorithm is commonly employed for constructing designs based on the MNL model. However, as a hill-climbing method, the CE algorithm tends to quickly converge to local optima, potentially limiting the quality of the resulting designs. We propose the use of a simulated annealing (SA) algorithm to construct Bayesian optimal designs. This algorithm accepts both superior and inferior solutions, avoiding premature convergence and allowing a more thorough exploration of potential solutions. Consequently, it ultimately obtains higher-quality choice designs compared to the CE algorithm. Our work represents the first application of an SA algorithm in constructing Bayesian optimal designs for DCEs. Through extensive computational experiments, we demonstrate that the SA designs generally outperform the CE designs in terms of statistical efficiency, especially when the prior preference information is highly uncertain.

关键词： Discrete choice experiments Bayesian optimal design algorithm comparison Simulated annealing Coordinate exchange

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Performance comparisons of Association Rule Learning algorithms on a Supermarket Data 11th

Performance Comparisons of Association Rule Learning Algorit...

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11st World Conference on Information Systems and Technologies (WorldCIST)

作者： Alawadh, Monerah Mohammed Barnawi, Ahmed Mohammed King Abdulaziz Univ Jeddah Saudi Arabia

ISBN: (纸本)9783031456411;9783031456428

In today's business environment, sustaining growth and profitability is never a guarantee. This concept of instability encourages business owners to seek new opportunities using technological and scientific advances to grasp the sustainability and maximizing profit. Nowadays, Machine Learning is helping the retail sector in many ways. Such improved life cycles of products, services, and business models. Association Rule Learning (ARL) is an unsupervised machine learning technique having great potential to extract interesting and useful associations and/or correlations among frequent item sets in transactional dataset. ARL techniques are widely employed in a variety of applications, including web click data, medical diagnostics, consumer behavior analysis, bioinformatics, and many more. In this paper, we conduct a comprehensive performance analysis of six distinct ARL algorithms, which was carried out to find the best fit algorithm for our data. The data is a large transactional dataset of one of the biggest supermarket chains in Saudi Arabia. Those algorithms are;Apriori, FP-Growth, GCD, Top-K Rules, TNR, and Closed AR. Note that these algorithms are uniquely different in terms of the number of datasets scanning which influences performance. The evaluation study shows that FP-growth algorithm based on FP-Tree outperformed other algorithms. FP-growth was more efficient and scalable than other algorithms for the given data in terms of memory usage and processing time.

关键词： Association Rule Learning algorithm comparison Machine Learning Apriori pattern reconstruction FP-Growth algorithm

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