Ensemble Learning Models for Classification and Selection of Web Services: A Review

Hasnain, Muhammad; Ghani, Imran; Jeong, Seung Ryul; Ali, Aitizaz

doi:10.32604/csse.2022.018300

Cited by 3 publications

(1 citation statement)

References 36 publications

(66 reference statements)

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“…In the classification phase, this paper compares the performance of several popular classification algorithms [45] on a sample-based evaluation, as shown in Table 3. Comparisons are performed on the DREAMS dataset using LOSO validation, with Union annotations as ground truth.…”

Section: Comparison Of Different Classifiersmentioning

confidence: 99%

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

Wang,

Li,

Wan

et al. 2023

Computers, Materials &Amp; Continua

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Sleep spindles are an electroencephalogram (EEG) biomarker of non-rapid eye movement (NREM) sleep and have important implications for clinical diagnosis and prognosis. However, it is challenging to accurately detect sleep spindles due to the complexity of the human brain and the uncertainty of neural mechanisms. To improve the reliability and objectivity of sleep spindle detection and to compensate for the limitations of manual annotation, this study proposes a new automatic detection algorithm based on Matching Pursuit (MP) and Least Squares Boosting (LSBoost), where the automatic sleep spindle detection algorithm can help reduce the visual annotation workload of sleep clinicians. Specifically, MP is a time-frequency analysis method suitable for extracting spindle wave characteristics, which can accurately locate spindle waves on a time-frequency plane. LSBoost is an ensemble learning classification method to deal with unbalanced data. Initially, the MP method is used to search for EEG segments that are possible spindle waves from the filtered raw EEG data. Then, the designed feature segments are thrown into the LSBoost classifier to further identify the real spindles from all candidates and output the final results. The proposed method is verified on the common public dataset DREAMS. The experiment results show that the sensitivity and F1-scores based on the sample-based assessments achieve 68.2% and 55.4%, respectively. Furthermore, the Recall and F1-score based on the event assessments are 83.8% and 70.8%, respectively. These results show that the proposed algorithm is robust to the subject changes in the DREAMS dataset. In addition, it improves the quality of sleep spindle detection, which is expected to assist the manual marking of experts.

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Section: Comparison Of Different Classifiersmentioning

confidence: 99%

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

Wang,

Li,

Wan

et al. 2023

Computers, Materials &Amp; Continua

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Holistic learning and optimization scheme for wireless networks

Husen,

Chaudary,

Ahmad

et al. 2024

Trans Emerging Tel Tech

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Future networks are expected to use Artificial Intelligence and machine learning applications intensely. The primary focus of machine learning techniques will be on intelligent automation and decision‐making based on information learned from network functions, resources, and users. The research community has studied several schemes for using machine learning in communications and networks in the last two decades. However, the schemes mainly focus on learning the state information of individual network functions and resources and performing their optimization in a standalone fashion. Recently, a study group from the International Telecommunication Union has outlined machine learning pipeline requirements. It addresses several aspects of machine learning operations, such as model selection, data sources, and action points. However, the mechanisms to define the state of network functions and resources for different network layers and domains are yet to be explored. Therefore, in addition to the above, specific studies are required for cross‐layer and administrative domain knowledge sharing and holistic optimization. Without considering the holistic view, applying machine learning to optimizing individual functions and resources may result in nonoptimal and unexpected behaviors instead of having any positive effect. The need for global and deep holistic learning has recently been identified in the literature; however, no implementation of the above scheme has been studied or evaluated for networks. This article proposes a novel approach, implementing global and deep holistic learning in wireless networks toward fulfilling the requirements of future intelligent networks. It also proposes an objective function‐based feature engineering for wireless networks. Experimental results of the proposed scheme show significant improvements in the network performance and accuracy of the machine learning models. Moreover, applications of transfer learning to base learners for network functions and resources can significantly expedite decision‐making with reduced computational costs.

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Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities

Namoun

Humayun

Benrhouma

et al. 2023

MTI

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Students with special needs should be empowered to use assistive technologies and services that suit their individual circumstances and environments to maximize their learning attainment. Fortunately, modern distributed computing paradigms, such as the Internet of Things (IoT), cloud computing, and mobile computing, provide ample opportunities to create and offer a multitude of digital assistive services and devices for people with disabilities. However, choosing the appropriate services from a pool of competing services while satisfying the unique requirements of disabled learners remains a challenging research endeavor. In this article, we propose an ensemble meta-learning model that ranks and selects the best IoT services while considering the diverse needs of disabled students within the educational context. We train and test our deep ensemble meta-learning model using two synthetically generated assistive services datasets. The first dataset incorporates 50,000 records representing the possible use of 12 learning activities, fulfilled by 60 distinct assistive services. The second dataset includes a range of 120,000 service ratings of seven quality features, including response, availability, successibility, latency, cost, quality of service, and accessibility. Our deep learning model uses an ensemble of multiple input learners fused using a meta-classification network shared by all the outputs representing individual assistive services. The model achieves significantly better results than traditional machine learning models (i.e., support vector machine and random forest) and a simple feed-forward neural network model without the ensemble technique. Furthermore, we extended our model to utilize the accessibility rating of services to suggest appropriate educational services for disabled learners. The empirical results show the acceptability of our assistive service recommender for learners with disabilities.

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Ensemble Learning Models for Classification and Selection of Web Services: A Review

Cited by 3 publications

References 36 publications

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

An Efficient Sleep Spindle Detection Algorithm Based on MP and LSBoost

Holistic learning and optimization scheme for wireless networks

Service Selection Using an Ensemble Meta-Learning Classifier for Students with Disabilities

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