[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119424-en":3,"doc-seo-119424-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119424,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Multi-objective optimization by Machine Learning - Master’s thesis","Solving multi-objective optimization with machine learning can improve applications such as multi-junction traffic management and stock portfolio optimization. This thesis targets a data-intensive domain: multi-objective optimization in trot horse harness racing, focusing specifically on the V75. A major part of the work covers data collection, preprocessing, and feature engineering. Prediction is split into single-race modeling and system selection, where a neural network estimates the horse’s finishing percentage and a greedy selector builds a wagering system by choosing horses deemed similar to the winner.","Multi-objective optimization by Machine Learning  \nMaster’s thesis in Computer science and engineering  \nHampus Hagstrand  \nDepartment of Computer Science and Engineering CHALMERS UNIVERSITY OF TECHNOLOGY UNIVERSITY OF GOTHENBURG  \nGothenburg, Sweden 2023  \nMaster’s thesis 2023  \nMulti-objective optimization by Machine Learning  \nHampus Hagstrand  \nDepartment of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Gothenburg, Sweden 2023  \nMulti-objective optimization by Machine Learning  \nHampus Hagstrand  \n© Hampus Hagstrand, 2023 .  \nSupervisor: Carl-Johan Seger  \nExaminer: Jean-Philippe Bernardy  \nMaster’s Thesis 2023  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg  \nTelephone +46 31 772 1000  \nTypeset in LATEX  \nGothenburg, Sweden 2023  \nMulti-objective optimization by Machine Learning  \nHampus Hagstrand  \nDepartment of Computer Science and Engineering  \nChalmers University of Technology and University of Gothenburg  \nAbstract  \nSolving multi-objective optimization with machine learning can significantly improve various fields, such as multi-junction traffic management or stock portfolio optimization. These are problems that can have a large amount of relevant and irrelevant data. This thesis targets one such problem area, focusing on multi-objective optimization in trot horse harness racing, specifically the V75 .  \nA large part of the project was data-related, such as data collection, preprocessing, and engineering. The predicting part is divided into two parts single race prediction and system predictions. The single-race prediction utilizes the large amount of data collected to train a neural network to predict the percentage of the horse finishing behind the winner. The system prediction uses the result from the neural network to pick a system. During this process, a greedy algorithm selects more horses in the races that the machine learning deems close and fewer that it deems one-sided.  \nThe performance evaluation showed that the single race predicting performed on par with the more advanced baseline and showed clear signs of finding a pattern between the data and the finishing result. The system prediction found some accuracy but did not surpass the odds baseline.  \nKeywords: Machine Learning, Artificial Intelligence, Multi-objective Optimization, Horse Racing, V75  \nAcknowledgements  \nFirstly, I would like to begin by expressing my sincere gratitude to Carl-Johan Seger, who has been an outstanding supervisor. His guidance, expertise, and patience were instrumental in completing this thesis.  \nSecondly, I would like to thank Jean-Philippe Bernardy for his insightful feedback on the half-time report. His critiques significantly improved the quality of my work.  \nThirdly, I want to extend my appreciation to Magnus Edvardsson, Per Klevmarken, and Anton Hålldén. They introduced me to the world of V75, a sport that formed the foundation of this thesis. Their wisdom, expertise, and knowledge about the game played a crucial role in shaping this work.  \nLastly, I would also like to acknowledge that Grammarly was used for spell and grammar checking, ensuring the readability and correctness of this thesis.  \nHampus Hagstrand, Gothenburg, 2023-06-27  \nContents  \nList of Figures xi  \n1 Introduction 1  \n1.1 Horse Racing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2  \n1.1.1 Trotting and V75 . . . . . . . . . . . . . . . . . . . . . . . . . 3  \n2 Background 5  \n2.1 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5  \n2.1. 1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . 6  \n2.2 Previous work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n2.2. 1 Sport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7  \n3 Methods 9  \n3. 1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n3.1. 1 Scraping . ","cbCaioiCS0nEGTTx","https://ap.wps.com/l/cbCaioiCS0nEGTTx","pdf",6409424,1,56,"English","en",105,"# Introduction\n## Horse Racing\n## Trotting and V75\n# Background\n## Machine learning\n## Neural Networks\n## Previous work\n## Sport\n# Methods\n## Data\n## Scraping\n## Preprocessing\n## Validator\n## Evaluation\n## Evaluator\n## Baselines\n## Machine learning models\n## TensorFlow and Keras\n## Multi-Event Decision Making\n## Greedy naive odds baseline\n## Greedy naive picking algorithm\n# Results\n## Single race predictions\n## Baselines\n## Random Baseline\n## Starting track\n## Win percentage\n## Experiment 1\n## Experiment 2","[{\"question\":\"What problem does the thesis address with machine learning?\",\"answer\":\"It addresses multi-objective optimization in data-rich settings, specifically trot horse harness racing focused on the V75.\"},{\"question\":\"How does the system prediction work in the proposed approach?\",\"answer\":\"It uses the neural network’s single-race prediction outputs and then applies a greedy selection strategy to choose horses for a wagering system.\"},{\"question\":\"How did the models perform compared with baseline methods?\",\"answer\":\"Single-race prediction performed on par with an advanced baseline and showed signs of learning patterns between data and finishing results, while system prediction achieved some accuracy but did not surpass the odds baseline.\"}]","Multi-objective optimization by Machine Learning - 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