基于优化规则的群体成本敏感决策树的不平衡数据分类

IF 5.8 3区 计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY
Mehdi Mansouri, Mohammad H. Nadimi-Shahraki, Zahra Beheshti
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引用次数: 0

摘要

尽管决策树(DT)在各种应用程序中广泛使用,但在处理不平衡数据集时,它们的性能往往会受到影响,其中某些类的分布明显超过其他类。代价敏感学习是解决这一问题的一种策略,目前已经提出了几种代价敏感的DT算法。然而,现有的启发式算法试图贪婪地选择一个更好的分裂点或特征节点,导致树节点的局部最优,而忽略了整个树的代价。此外,成本的确定是困难的,通常需要领域的专业知识。本研究利用代价敏感学习策略和增强的基于群的算法,提出了一种针对不平衡数据的代价敏感DT (SCDT)。使用混合个体表示对DT进行编码。设计了一种混合人工蜂群方法,在基于f测度的适应度函数中考虑指定的成本,对规则进行优化。实验结果表明,SCDT方法在大多数数据集上取得了最高的性能。此外,SCDT在召回率、准确率、f1得分和AUC等其他关键性能指标上也表现出色,结果显著,平均值分别为83%、87.3%、85%和80.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Swarm-based Cost-sensitive Decision Tree Using Optimized Rules for Imbalanced Data Classification

Swarm-based Cost-sensitive Decision Tree Using Optimized Rules for Imbalanced Data Classification

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.

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来源期刊
Journal of Bionic Engineering
Journal of Bionic Engineering 工程技术-材料科学:生物材料
CiteScore
7.10
自引率
10.00%
发文量
162
审稿时长
10.0 months
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.
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