QoS-Based Architecture for Discovery and Selection of suitable Web Services Using Non-functional properties

Rangarajan, Sarathkumar; Chandar, Ravindra Krishna

doi:10.4108/eai.18-1-2017.152102

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…[7,8]. Therefore, selecting a service that can meet clients' quality standards from a range of services has become a crucial challenge [9,10]. Each web service is designed to perform a specific task, and a user's workflow may consist of multiple tasks.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Bonab,

Tanha,

Masdari

2024

IEEE Access

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The Internet provides a platform for sharing services, and web service brokers help users to choose the suitable service among similar services based on ranking. The quality of service is important in evaluating the services the user needs. However, finding a quality-based data label in many fields can be time-consuming and difficult. Thus, machine learning is required to classify and choose the best service in this field. The selection process is done through analysis and recommendations by the system. This article introduces the SSL-WSC algorithm, which classifies unlabeled data through semi-supervised self-training learning using a small amount of labeled data. This algorithm labels the data using a two-step method of calculating a score for each service and dynamic thresholding. The quality features of web services obtained from the QWS dataset were used to evaluate the performance of the proposed algorithm. The experimental results in different scenarios showed that using proposed semi-supervised learning algorithms to create classification models led to better results, so it improved the F1-score, accuracy, and precision, on average, by 11.26%, 9.43% and 9.53%, respectively, as compared to the supervised method.

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Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Bonab,

Tanha,

Masdari

2024

IEEE Access

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“…These classification algorithms typically assume their datasets are balanced in their class distribution. However, in many real-world application domains such as medical diagnosis [3,[11][12][13][14], streaming and social behavior data analysis [2,15], software development process [16,17], financial frauds [18][19][20], unsolicited phone calls [21], disaster risks [22,23], recommendation systems [24,25], and text classification [26][27][28], inherently imbalanced datasets are commonly seen. Also, the limitation of the data collection process and the imbalanced cost for fixing different errors can lead to the imbalance.…”

Section: Introductionmentioning

confidence: 99%

Experimental Comparison of Classification Methods under Class Imbalance

Chen¹,

Ji²

2021

ICST Transactions on Scalable Information Systems

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The class imbalance problem is prevalent in many domains including medical, natural language processing, image recognition, economic and geographic areas etc. We perform a systematic experimental comparison of different imbalance classification algorithms -ranging from sampling, distance metric learning, costsensitive learning to ensemble learning approaches -on several datasets from UCI, KEEL and OpenML. The algorithms included DDAE, MWMOTE, SMOTE, RUSBoost, AdaBoost, cost-sensitive decision tree (csDCT), self-paced Ensemble Classifier, MetaCost, CAdaMEC and Iterative Metric Learning (IML). As the substantial bias potentially caused by imbalance classification can be harmful for underrepresented classes which are of critical social and economic values and even lives, the main objective of our study is thus to understand the impact of imbalance ratio and the size of the utilized datasets on the performance of the above-mentioned algorithms. Our experiments show that 1) Sampling methods perform the worst and cannot be used directly for imbalanced classification, since they lack of consideration of neighborhoods based on distance. However, some classifiers can be improved after the balance of class distribution. 2) Cost-sensitive learning models should be utilized when the dataset is less imbalanced, because it is difficult to set an appropriate cost matrix for a specific dataset, which can cause performance fluctuations. 3) IML consistently shows good performance (in terms of F1 and AUCPRC), is resilient to different imbalance ratios but sensitive to the data distribution of the dataset. 4) Ensemble learning techniques generally perform better over other approaches due to their combined intelligence of multiple basic classifiers. 5) In terms of system performance, self-paced Ensemble Classifier performs fairly well with regards to learning time, while IML and DDAE yield the longest learning time; AdaBoost and self-paced Ensemble Classifier are two algorithms require lowest memory usage. We also provide our empirical recommendation for algorithm selection under different requirements and usage scenarios based on our analysis.

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The Study of Web Service Discovery: A Clustering and Differential Evolution Algorithm Approach

Huang

Zhao

2019

2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference On

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QoS-Based Architecture for Discovery and Selection of suitable Web Services Using Non-functional properties

Cited by 4 publications

References 5 publications

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

A Semi-Supervised Learning Approach to Quality-Based Web Service Classification

Experimental Comparison of Classification Methods under Class Imbalance

The Study of Web Service Discovery: A Clustering and Differential Evolution Algorithm Approach

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