{"title":"交叉验证的向量广义加性模型多标签分类","authors":"Amri Muhaimin, Wahyu Wibowo, Prismahardi Aji Riyantoko","doi":"10.32890/jict2023.22.4.5","DOIUrl":null,"url":null,"abstract":"Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously.However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This studywas conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) toaddress these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametricmodel combining both parametric and non-parametric components as the underlying base model. Cross-validation was also appliedto tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with atree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metricscores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, butthese improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classificationtasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation\",\"authors\":\"Amri Muhaimin, Wahyu Wibowo, Prismahardi Aji Riyantoko\",\"doi\":\"10.32890/jict2023.22.4.5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously.However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This studywas conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) toaddress these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametricmodel combining both parametric and non-parametric components as the underlying base model. Cross-validation was also appliedto tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with atree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metricscores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, butthese improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classificationtasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2023-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32890/jict2023.22.4.5\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32890/jict2023.22.4.5","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Multi-label Classification Using Vector Generalized Additive Model via Cross-Validation
Multi-label classification is a unique challenge in machine learning designed for two targets with each containing one or multiple classes. This problem can be resolved using several methods, including the classification of the targets individually or simultaneously.However, most models cannot classify the target simultaneously, and this is not expected to happen in the modeling rule. This studywas conducted to propose a novel solution in the form of a Vector Generalized Additive Model Using Cross-Validation (VGAMCV) toaddress these problems. The proposed method leverages the Vector Generalized Additive Model (VGAM), which is a semi-parametricmodel combining both parametric and non-parametric components as the underlying base model. Cross-validation was also appliedto tune the parameters to optimize the performance of the method. Moreover, the methodology of VGAMCV was compared with atree-based model, Random Forest, commonly used in multi-label classification to evaluate its effectiveness based on fourteen metricscores. The results showed positive outcomes as indicated by 0.703 average accuracy and 0.601 Area Under Curve (AUC) recorded, butthese improvements were not statistically significant. Meanwhile, the method offered a viable alternative for multi-label classificationtasks, and its introduction served as a contribution to the expanding repertoire of methods available for this purpose.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.