[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118393-en":3,"doc-seo-118393-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118393,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Genetic programming in machine learning - based on the evaluation of house affordability classification","Automated machine learning using genetic programming (GP) is evaluated for modeling and prediction of house affordability from real survey-based datasets. The study addresses practical dataset challenges such as class imbalance, weak correlations, and outliers by focusing on GP hyper-parameter selection, particularly population size. Experiments use hold-out testing to assess predictive performance on classification, reaching about 70% accuracy under a split ratio of 0.2 with GP population size 30, supporting faster and more accurate real-data applications.","Genetic programming in machine learning based on the evaluation of house affordability classification  \nSuraya Masrom1, Norhayati Baharun2, Nor Faezah Mohamad Razi2, Abdullah Sani Abd Rahman3,  \nNor Hazlina Mohammad2, Nor Aslily Sarkam2  \n1Computer Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perak Branch, Perak,  \nMalaysia  \n2Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perak Branch,  \nPerak, Malaysia  \n3Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Perak, Malaysia  \nArticle history:  \nReceived Sep 22, 2023 Revised Feb 24, 2024 Accepted Mar 31, 2024  \nKeywords:  \nCrossover rate Genetic programming House affordability Machine learning Mutation rate Population size  \nCorresponding Author:  \nOne of the big challenges in machine learning is difficulty of achieving high accuracy in a short completion time. A more difficulties appeared when the algorithm needs to be used for solving real dataset from the survey-based data collection. Imbalance dataset, insufficient strength of correlations, and outliers are common problems in real dataset. To accelerate the modelling processes, automated machine learning based on meta-heuristics optimization such as genetic programming (GP) has started to emerge and is gaining popularity. However, identifying the best hyper-parameters of the meta-heuristics’ algorithm is the critical issue. This paper demonstrates the evaluation of GP hyper-parameters in modeling machine learning on house affordability dataset. The important hyper-parameters of GP are population size (PS), that has been observed with different setting in this research. The machine learning with GP was used to predict house affordability among employers with transport expenditure and job mobility as some of the attributes. The results from testing that run on hold-out samples show that GP machine learning can reach to 70% accuracy with split ratio 0.2 and GP PS 30. This research contributes to the advancement of automated machine learning techniques, offering potential for faster and more accurate real survey-based datasets.  \nThis is an open access article under the CC BY-SA license.  \nNorhayati Baharun  \nMathematical Sciences Studies, College of Computing, Informatics and Mathematics Universiti Teknologi MARA Perak Branch  \n35400 Tapah Road, Perak, Malaysia  \n[Email: norha603@uitm.edu.my](Email: norha603@uitm.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nMachine learning has been so prevalent in various domains of real-world problems due to the evolution of industrial 4.0. The ever-increasing realm of machine learning has helped industries, businesses, government agencies, public, and private people in making fast decision for simple and complex problems. In medical [1], [2], education [3]–[6], agriculture [7], finance and economy [8], [9], building and property [10]–[12], as well as in engineering [13], the utilization of machine learning is highly substantial. As a result, critical demands are needed to simplify the implementation complexity of machine learning to be used by inexpert or inexperienced data scientists from various research fields. To introduce rapid tools for the novice machine learning users is highly significant.  \nGenetic programming (GP) [14] is a popular meta-heuristic [15] algorithm that can be used to automate some important tasks in machine learning. Most research on automated machine learning that used GP aim to automate features selection task. GP based feature selection was introduced in [16] for optimizing the selection of machine learning parameters when tested on the particular problems. Focus on highdimensional data, Ma and Gao [14] presented multi-features construction based on multi-tree GP representation to resolve single-features GP representation. Multi-features construction provides multi-layers of features mimics to deep learning neural networ","cbCair01o8ZQjGwj","https://ap.wps.com/l/cbCair01o8ZQjGwj","pdf",466111,1,"English","en",105,"# Article Info\n## Introduction\n## Related Work and Motivation\n## Dataset and Problem Definition","[{\"question\":\"What issue does the paper focus on in genetic programming-based machine learning?\",\"answer\":\"The paper focuses on evaluating which GP hyper-parameters lead to strong performance when building a machine learning model for house affordability classification.\"},{\"question\":\"Which GP hyper-parameter is highlighted as important in the research?\",\"answer\":\"Population size (PS) is highlighted as a key GP hyper-parameter and is tested with different settings.\"},{\"question\":\"What predictive performance is reported for the final GP model?\",\"answer\":\"Hold-out testing results report about 70% accuracy using a split ratio of 0.2 and GP population size 30.\"}]","Genetic programming in machine learning - 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