Mehdi Mansouri, Mohammad H. Nadimi-Shahraki, Zahra Beheshti
{"title":"Swarm-based Cost-sensitive Decision Tree Using Optimized Rules for Imbalanced Data Classification","authors":"Mehdi Mansouri, Mohammad H. Nadimi-Shahraki, Zahra Beheshti","doi":"10.1007/s42235-025-00673-0","DOIUrl":null,"url":null,"abstract":"<div><p>Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"22 3","pages":"1434 - 1458"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bionic Engineering","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s42235-025-00673-0","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
期刊介绍:
The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to:
Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion.
Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials.
Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices.
Development of bioinspired computation methods and artificial intelligence for engineering applications.