[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119949-en":3,"doc-seo-119949-105":30,"detail-sidebar-cat-0-en-105":91},{"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":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},119949,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Identifying Important Features for Exoplanet Detection - A Machine Learning Approach","Exoplanet discovery has intensified through large-scale observations from ground and space-based programs such as NASA’s Kepler, yet confirming the growing number of candidates remains difficult. This study presents a statistical and machine learning pipeline to identify informative features for exoplanet identification using the Kepler Cumulative Object of Interest (KCOI) dataset. After preprocessing, ANOVA F-test, Mutual Information Gain, and Recursive Feature Elimination are applied, followed by training state-of-the-art classifiers recursively. Results show the best performance when features selected by RFE with Random Forest are used, with CatBoost reaching 99.61% accuracy, supporting efficient and accurate verification in large astronomical datasets.","e-ISSN 2805-4356 p-ISSN 2805-4364  \n10.5281/zenodo.10566250 Vol. 07 Issue 01 Jan-2024 Manuscript ID: \\#01201  \nIdentifying Important Features for Exoplanet Detection: A Machine Learning Approach  \nAbdul Karim1*, Jamal Uddin1 and Md. Mahmudul Hasan Riyad1  \n1Department of Applied Mathematics, Noakhali Science and Technology University, Noakhali-3814, Bangladesh.  \nAbstract  \nThe study and discovery of exoplanets (planets outside the solar system) have been a major focus in astronomy. Many efforts have been made to discover exoplanets using ground based and space based observatory, NASA’s Exoplanet Exploration Program being one of them. It has developed modern satellites like Kepler which are capable of collecting large array of data to help researchers with these objects. With the increasing number of exoplanet candidates, identifying and verifying their existence becomes a challenging task. In this research, we propose a statistical and machine learning approach to identify important features for exoplanet identification. For this purpose, we use the Kepler Cumulative Object of Interest (KCOI) dataset. After pre-processing the data we utilize statistical methods namely ANOVA F-test, Mutual Information Gain (MIG), Recursive Feature Elimination (RFE) to select the most significant features and have trained  \n10 state-of-the-art classifiers on them recursively to identify the features that leads to best performance. According to the results of our investigation, classifierstrained on features chosen by Recursive Feature Elimination with Random Forest as estimator produces superior results, with CatBoost classifier being the best with an accuracy of 99.61%. Our findings demonstrate the potential of machine learning in helping astronomers to efficiently and accurately verify exoplanet candidates in large astronomical datasets.  \nKeywords  \nExoplanet, Machin Learning, KCOI, Important Features.  \nThis work is licensed under Creative Commons Attribution 4.0 License.  \nPage 01 of 17  \n1 INTRODUCTION  \nOne of the most ancient natural sciences in human history is astronomy. For centuries, people have gazed up at the night sky to see the dazzling stars. The observable universe contains hundreds of billions of galaxies each of which contains billons of stars and these stars also have their own planetary system. More intriguing questions emerged as our understanding of the cosmos increased. Exoplanets were discovered as a result of people’s interest about whether planets similar to our own existed in other star systems.  \nExoplanet discovery is a tedious and time-consuming process that typically involves a team of professionals who devote their lives to it. Usually, they use data collected from ground based observatories and satellite-based telescopes together with their expertise, intellect and perseverance to hunt for exoplanets. But with the launch of specialized satellites like Kepler to discover exoplanets this process became much simpler. These satellites capture and process images and generate usable data for the scientists to interrogate them with little to no processing needed.  \nThere has been a lot of work done to identify exoplanets using machine learning but not much has been done to identify the important features which leads to the identification. In this study, we explore the publicly available Kepler cumulative object of interest (KCOI)[1] dataset. This dataset contains information collected by the Kepler satellite and stored into several fields. We use this data to identify the important features to improve the accuracy of identifying exoplanets using statistical methods and machine learning.  \nThe detection and characterization of exoplanets is a rapid growing field of research, and the use of machine learning techniques becomes increasingly popular in recent years. In the context of exoplanet detection, machine learning is being used to analyze large datasets from telescopes and identify patterns that indicate the presen","cbCaikK1mZc1vDV6","https://ap.wps.com/l/cbCaikK1mZc1vDV6","pdf",1802394,1,17,"English","en",105,"# Abstract\n# 1 Introduction\n## Exoplanet discovery challenges\n## Machine learning for exoplanet detection\n# 2 Background\n## 2.1 NASA's Exoplanet Mission","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To identify the most important features that improve exoplanet detection by using statistical methods and machine learning on the Kepler KCOI dataset.\"},{\"question\":\"Which dataset is used for training and feature selection?\",\"answer\":\"The Kepler Cumulative Object of Interest (KCOI) dataset collected by the Kepler satellite.\"},{\"question\":\"Which method and classifier provide the best reported accuracy?\",\"answer\":\"Recursive Feature Elimination (RFE) with Random Forest as the estimator leads to strong results, and CatBoost achieves the best accuracy of 99.61%.\"}]","Identifying Important Features for Exoplanet Detection - A Machine Learning Approach | PDF",1785727146,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identifying-important-features-for-exoplanet-detection-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-important-features-for-exoplanet-detection-a-machine-learning-approach/119949/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this research?","Question",{"text":75,"@type":76},"To identify the most important features that improve exoplanet detection by using statistical methods and machine learning on the Kepler KCOI dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset is used for training and feature selection?",{"text":80,"@type":76},"The Kepler Cumulative Object of Interest (KCOI) dataset collected by the Kepler satellite.",{"name":82,"@type":73,"acceptedAnswer":83},"Which method and classifier provide the best reported accuracy?",{"text":84,"@type":76},"Recursive Feature Elimination (RFE) with Random Forest as the estimator leads to strong results, and CatBoost achieves the best accuracy of 99.61%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]