Imbalanced data preprocessing model for web service classification

IF 1.6 Q2 ENGINEERING, MULTIDISCIPLINARY International Journal of System Assurance Engineering and Management Pub Date : 2024-08-28 DOI:10.1007/s13198-024-02485-7
Wasiur Rhmann, Amaan Ishrat
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Abstract

Web services are a novel method of web application development. They allow business to adapt to a new environment and change quickly according to customer needs. The client requires high-quality web services with minimal response time, more security, and high availability. With the increasing demand for web services, the introduction of web services rapidly in the business environment has influenced rapidly the web service quality. In the present work, a novel model for web service classification is proposed. Three metaheuristic techniques: Whale optimization algorithm, Simulated annealing algorithm, and Ant colony optimization are used to select the best subset of features. Web service-based imbalanced dataset is balanced using SMOTETomek (Synthetic minority oversampling + Tomek link). Ensemble Adaboost and Gradient boosting algorithms are used for the creation of a web service prediction model. The publicly available QWS dataset is used for experimental purposes. The results of the proposed models are compared with machine learning techniques. It was observed that the Ant colony algorithm performed best for relevant feature selection and the Ensemble Adaboost and Gradient boosting algorithm outperformed all other machine learning techniques for web service classification.

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用于网络服务分类的不平衡数据预处理模型
网络服务是一种新颖的网络应用程序开发方法。网络服务使企业能够适应新的环境,并根据客户需求迅速做出改变。客户需要响应时间最短、安全性更高、可用性更强的高质量网络服务。随着网络服务需求的不断增加,网络服务在商业环境中的快速引入也迅速影响了网络服务质量。本研究提出了一种新的网络服务分类模型。三种元启发式技术:鲸鱼优化算法、模拟退火算法和蚁群优化被用来选择最佳特征子集。使用 SMOTETomek(合成少数超采样 + Tomek 链接)平衡基于网络服务的不平衡数据集。使用集合 Adaboost 和梯度提升算法创建网络服务预测模型。实验使用了公开的 QWS 数据集。所提模型的结果与机器学习技术进行了比较。结果表明,蚁群算法在相关特征选择方面表现最佳,而在网络服务分类方面,Ensemble Adaboost 和 Gradient boosting 算法的表现优于所有其他机器学习技术。
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来源期刊
CiteScore
4.30
自引率
10.00%
发文量
252
期刊介绍: This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems. Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.
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