{"title":"A dual algorithmic approach to deal with multiclass imbalanced classification problems","authors":"S. Sridhar , S. Anusuya","doi":"10.1016/j.bdr.2024.100484","DOIUrl":null,"url":null,"abstract":"<div><p>Many real-world applications involve multiclass classification problems, and often the data across classes is not evenly distributed. Due to this disproportion, supervised learning models tend to classify instances towards the class with the maximum number of instances, which is a severe issue that needs to be addressed. In multiclass imbalanced data classification, machine learning researchers try to reduce the learning model's bias towards the class with a high sample count. Researchers attempt to reduce this unfairness by either balancing the data before the classifier learns it, modifying the classifier's learning phase to pay more attention to the class with a minimum number of instances, or a combination of both. The existing algorithmic approaches find it difficult to understand the clear boundary between the samples of different classes due to unfair class distribution and overlapping issues. As a result, the minority class recognition rate is poor. A new algorithmic approach is proposed that uses dual decision trees. One is used to create an induced dataset using a PCA based grouping approach and by assigning weights to the data samples followed by another decision tree to learn and predict from the induced dataset. The distinct feature of this algorithmic approach is that it recognizes the data instances without altering their underlying data distribution and is applicable for all categories of multiclass imbalanced datasets. Five multiclass imbalanced datasets from UCI were used to classify the data using our proposed algorithm, and the results revealed that the duo-decision tree approach pays better attention to both the minor and major class samples.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"38 ","pages":"Article 100484"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579624000595","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Many real-world applications involve multiclass classification problems, and often the data across classes is not evenly distributed. Due to this disproportion, supervised learning models tend to classify instances towards the class with the maximum number of instances, which is a severe issue that needs to be addressed. In multiclass imbalanced data classification, machine learning researchers try to reduce the learning model's bias towards the class with a high sample count. Researchers attempt to reduce this unfairness by either balancing the data before the classifier learns it, modifying the classifier's learning phase to pay more attention to the class with a minimum number of instances, or a combination of both. The existing algorithmic approaches find it difficult to understand the clear boundary between the samples of different classes due to unfair class distribution and overlapping issues. As a result, the minority class recognition rate is poor. A new algorithmic approach is proposed that uses dual decision trees. One is used to create an induced dataset using a PCA based grouping approach and by assigning weights to the data samples followed by another decision tree to learn and predict from the induced dataset. The distinct feature of this algorithmic approach is that it recognizes the data instances without altering their underlying data distribution and is applicable for all categories of multiclass imbalanced datasets. Five multiclass imbalanced datasets from UCI were used to classify the data using our proposed algorithm, and the results revealed that the duo-decision tree approach pays better attention to both the minor and major class samples.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.