[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126576-en":3,"doc-seo-126576-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126576,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","SPINEX - Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning","Machine learning advances increasingly enable accurate prediction, yet many methods remain difficult to interpret and often struggle when data are high-dimensional or imbalanced. SPINEX is a similarity-based interpretable neighbor exploration algorithm proposed to address these limitations. The method integrates ensemble learning with feature interaction analysis to improve predictive quality while quantifying each feature’s contribution and revealing feature interactions. Experiments on 59 datasets for regression and classification show comparable or better performance in some settings, demonstrating practical value for real-world applications.","SPINEX: Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning  \nM.Z. Naser 1,2, Mohammad Khaled al-Bashiti1, A.Z. Naser3  \n1School of Civil & Environmental Engineering and Earth Sciences (SCEEES), Clemson University, USA 2Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University, USA  \n[E-mail:](E-mail: mznaser@clemson.edu)[ ](E-mail: mznaser@clemson.edu)[mznaser@clemson.edu](E-mail: mznaser@clemson.edu), [malbash@g.clemson.edu](malbash@g.clemson.edu), [Website:](Website: www.mznaser.com)[ ](Website: www.mznaser.com)[www.mznaser.com](Website: www.mznaser.com)  \n3Department of Mechanical Engineering, University of Guelph, Canada, E-mail: [anaser@uoguelph.ca](anaser@uoguelph.ca)  \nAbstract  \nThe field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algorithm combines ensemble learning and feature interaction analysis to achieve accurate predictions and meaningful insights by quantifying each feature's contribution to predictions and identifying interactions between features, thereby enhancing the interpretability of the algorithm. To evaluate the performance of SPINEX, extensive experiments on 59 synthetic and real datasets were conducted for both regression and classification tasks. The results demonstrate that SPINEX achieves comparative performance and, in some scenarios, may outperform commonly adopted ML algorithms. The same findings demonstrate the effectiveness and competitiveness of SPINEX, making it a promising approach for various real-world applications.  \nKeywords: Algorithm; Machine learning; Interpretability, Supervised learning.  \n1.0 Introduction  \nThe rapid growth of machine learning (ML) techniques has revolutionized various domains, enabling accurate predictions and decision-making [1,2] . In particular, regression and classification algorithms play a pivotal role in extracting valuable insights from data. However, challenges persist, such as the lack of interpretability in complex models [3] . This has led to a growing interest in interpretable ML algorithms [4] . While existing approaches, such as decision trees and linear models, offer interpretability, they often sacrifice predictive performance. Conversely, complex models like neural networks and ensemble methods achieve high accuracy but lack interpretability [5] .  \nIn the vast landscape of algorithmic design, similarity-based algorithms are potent methods for tackling various problems [6,7] . These algorithms rely on the premise that similar objects share similar properties and hence draw their strength from leveraging instance similarities and proximity to make predictions or categorizations/clustering. This approach is particularly useful in recommendation systems, classification tasks, and regression problems, where the goal is to predict an outcome based on the similarity of the input data to previously seen examples.  \nThe core idea behind such models is to find the 'neighbors' of a given data point in the feature space. These neighbors are other data points similar to the given instance at hand based on some similarity measure. Once these neighbors are identified, they are used to predict the given data point. This is done by taking a weighted average of the neighbors' outcomes, where the weights are determined by the similarity of each neighbor to the given data point. This underlying principle  \nis harnessed in various contexts, such as document retrieval [8], image recognition [9], etc. The key challenge in realizing such a concept lies in defining \"similarity\". Traditionally, different similarity measures might be used, such as Euclidean distance, ","cbCaienDeoilAfms","https://ap.wps.com/l/cbCaienDeoilAfms","pdf",2914671,2,1,37,"English","en",105,"# Introduction\n## Interpretable ML and existing trade-offs\n## Similarity-based algorithms and neighbor concept\n## Similarity measure and neighborhood size\n## Explainability advantages\n## Challenges: high dimensionality and data imbalance\n## Nearest-neighbor foundations","[{\"question\":\"What limitations does SPINEX aim to address in machine learning models?\",\"answer\":\"SPINEX targets the lack of interpretability in many ML algorithms and the difficulties that arise with high-dimensional and imbalanced data.\"},{\"question\":\"How does SPINEX produce predictions and explanations?\",\"answer\":\"SPINEX explores neighbors using similarity in feature space, then forms predictions via neighbor outcomes weighted by similarity. It also quantifies each feature’s contribution and analyzes feature interactions for interpretability.\"},{\"question\":\"How was SPINEX evaluated and what were the results?\",\"answer\":\"The paper reports extensive experiments on 59 synthetic and real datasets for both regression and classification tasks. Results show comparable performance and, in some cases, superiority over commonly used ML algorithms.\"}]","SPINEX - Similarity-based Predictions and Explainable Neighbors Exploration for Regression and Classification Tasks in Machine Learning | PDF",1785933438,93,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"spinex-similarity-based-predictions-and-explainable-neighbors-exploration-for-regression-and-classification-tasks-in-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/spinex-similarity-based-predictions-and-explainable-neighbors-exploration-for-regression-and-classification-tasks-in-machine-learning/126576/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What limitations does SPINEX aim to address in machine learning models?","Question",{"text":76,"@type":77},"SPINEX targets the lack of interpretability in many ML algorithms and the difficulties that arise with high-dimensional and imbalanced data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does SPINEX produce predictions and explanations?",{"text":81,"@type":77},"SPINEX explores neighbors using similarity in feature space, then forms predictions via neighbor outcomes weighted by similarity. It also quantifies each feature’s contribution and analyzes feature interactions for interpretability.",{"name":83,"@type":74,"acceptedAnswer":84},"How was SPINEX evaluated and what were the results?",{"text":85,"@type":77},"The paper reports extensive experiments on 59 synthetic and real datasets for both regression and classification tasks. 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