[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121686-en":3,"doc-seo-121686-105":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},121686,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction - Honors Thesis - 2023","Research related to Biology often relies on machine learning models that remain difficult for domain experts to interpret. This undergraduate honors thesis applies feature selection and rule extraction to a biochemistry classification task using High-Performance Thin Layer Chromatography (HPTLC) images. Eight views are generated from five Glycyrrhiza (licorice) species under varying conditions. Feature-selection methods and rule extraction reduce an initial feature space of about one thousand candidates to only ten critical features, enabling more interpretable decision-tree rules.","University of Mississippi  \neGrove  \n\n| Honors Theses | Honors College (Sally McDonnell Barksdale Honors College) |\n| --- | --- |\n| Spring 5-12-2023\u003Cbr>Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction\u003Cbr>Bozidar-Brannan Kovachev\u003Cbr>Follow this and additional works at: [https://egrove.olemiss.edu/hon_thesis](https://egrove.olemiss.edu/hon_thesis)\u003Cbr> Part of the Theory and Algorithms Commons |  |\n\nRecommended Citation  \nKovachev, Bozidar-Brannan, \"Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction\" (2023) . Honors Theses. 2841.  \n[https://egrove.olemiss.edu/hon_thesis/2841](https://egrove.olemiss.edu/hon_thesis/2841)  \nThis Undergraduate Thesis is brought to you for free and open access by the Honors College (Sally McDonnell Barksdale Honors College) at eGrove. It has been accepted for inclusion in Honors Theses by an authorized administrator of eGrove. For more information, please [contact egrove@olemiss.edu](contact egrove@olemiss.edu).  \nExploration of Feature Selection Techniques in Machine Learning Models  \non HPTLC Images for Rule Extraction  \nby  \nBrannan Kovachev  \nA thesis submitted to the faculty of The University of Mississippi in partial fulfillment of the requirements of the Sally McDonnel Barksdale Honors College  \nOxford  \nMay 2023  \nApproved by  \n\n| Advisor: Dr. Yixin Chen |\n| --- |\n| Reader: Dr. Feng Wang |\n\nReader: Dr. Thai Le  \n© 2023  \nBrannan Kovachev ALL RIGHTS RESERVED  \nABSTRACT  \nResearch related to Biology often utilizes machine learning models that are ultimately uninterpretable by the researcher. It would be helpful if researchers could leverage the same computing power but instead gain specific insight into decision-making to gain a deeper understanding of their domain knowledge. This paper seeks to select features and derive rules from a machine learning classification problem in biochemistry. The specific point of interest is five species of Glycyrrhiza, or Licorice, and the ability to classify them using High-Performance Thin Layer Chromatography (HPTLC) images. These images were taken using HPTLC methods under varying conditions to provide eight unique views of each species. Each view contains 24 samples with varying counts ofthe individual species. There are a few techniques applied for feature selection and rule extraction. The first two are based on methods recently pioneered and presented as “Binary Encoding of Random Forests” and “Rule Extraction using Sparse Encoding”(Liu 2012). In addition, an independently developed technique called“Interval Extraction and Consolidation” was applied, which was conceptualized due to the particular nature of the dataset. Altogether, these techniques used in consort with standard machine learning models could narrow a feature space from around one-thousand candidates to only ten. These ten most critical features were then used to derive a set of rules for the classification of the five species of licorice. Regarding feature selection, compared to standard model parameter optimization, the Binary Encoding of Random Forests performed similarly, if not much better, in reducing the feature space in almost all cases. Additionally, the application of Interval Extraction and Consolidation excelled in further simplifying the reduced feature space, often by another factor of five to ten. The selected features were then used for relatively simple rule extraction using decision trees, allowing for a more interpretable model.  \nACKNOWLEDGEMENTS  \nI would like to thank Dr. Yixin Chen for serving as my thesis advisor and providing invaluable assistance, guidance, and feedback throughout the entire process. I would like to thank Dr. Chittiboyina for providing us with the dataset and a problem to explore. Finally, thankyou to all the teachers and mentors who have helped shape the foundation of knowledge this paper was built on.  \nTABLE OF CONTENTS  \nABSTRACT...","cbCaisWYlhbSec5W","https://ap.wps.com/l/cbCaisWYlhbSec5W","pdf",790500,1,49,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGEMENTS\n# LIST OF FIGURES\n# LIST OF TABLES\n# SECTION 1-INTRODUCTION\n# SECTION 2-DATA\n## SECTION 3-DATA PREPARATION\n## SECTION 4 – MOTIVATIONS FOR MULTIPLE VIEWS\n## SECTION 5-METHODS FOR FEATURE SELECTION AND RULE EXTRACTION\n## SECTION 6-RANDOM FOREST PARAMETER OPTIMIZATION\n## SECTION 7-ALTERNATIVE IMPLEMENTATION FOR DETERMINING AN INTERVAL’S CENTER\n## SECTION 8-POSSIBLE EXTENSIONS AND FUTURE WORK","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"It addresses how to make biology-related machine learning decisions more interpretable by selecting informative features and extracting rules from a biochemistry classification task.\"},{\"question\":\"How are the HPTLC images and samples structured?\",\"answer\":\"The study classifies five Glycyrrhiza (licorice) species using HPTLC images created under varying conditions, producing eight unique views with multiple samples per view.\"},{\"question\":\"Which techniques are used for feature selection and rule extraction?\",\"answer\":\"The thesis applies feature-selection and rule-extraction approaches including methods based on “Binary Encoding of Random Forests” and “Rule Extraction using Sparse Encoding,” plus an “Interval Extraction and Consolidation” technique tailored to the dataset.\"}]","Exploration of Feature Selection Techniques in Machine Learning Models on HPTLC Images for Rule Extraction - 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