A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services

Alizadehsani, Zakieh; Plaza-Martínez, Pablo; González, Guillermo Hernández; González‐Briones, Alfonso; Chamoso, Pablo; Corchado, Juan M.

doi:10.1007/978-3-030-85710-3_8

Cited by 6 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Approaching these limitations requires the combination of different algorithms and Big Data analytics such as the combination of a supervised ML model as Random Forest (RF) with other non-supervised models such as Hierarchical Clustering (HCA) and Fuzzy Deformable Prototypes (FDP) to overcome the confidence and data complexity problems [ 38 ]. The HC has been used as a step prior to RF training to reduce features in very complex and high dimensional datasets [ 41 , 42 ]. Additionally, and despite its limitations [ 30 ], black-box models have been used to interpret the predictions of complex ML algorithms such as deducing the patterns learned by deep neural networks to understand how the algorithm works once trained and to detect biases [ 43 ].…”

Section: Potential Applicationsmentioning

confidence: 99%

Machine learning in infectious diseases: potential applications and limitations

Al Meslamani,

Sobrino,

de la Fuente

2024

Annals of Medicine

View full text Add to dashboard Cite

Section: Potential Applicationsmentioning

confidence: 99%

Machine learning in infectious diseases: potential applications and limitations

Al Meslamani,

Sobrino,

de la Fuente

2024

Annals of Medicine

View full text Add to dashboard Cite

“…In recent years, studies have focused on using AI-based techniques [18], [20], [23]. Although most earlier approaches have used information extraction for extracting service features from WSDL [15], [21], [22], [24], REST has become the prevalent solution for providing web services and APIs [25]. In RESTful service implementations, service description text data has become a significant feature in service classification.…”

Section: Proposed Classification Approachmentioning

confidence: 99%

Service Classification through Machine Learning: Aiding in the Efficient Identification of Reusable Assets in Cloud Application Development

Alizadehsani

Feitosa

Maikantis

et al. 2022

2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

Self Cite

View full text Add to dashboard Cite

Developing software based on services is one of the most emerging programming paradigms in software development. Service-based software development relies on the composition of services (i.e., pieces of code already built and deployed in the cloud) through orchestrated API calls. Black-box reuse can play a prominent role when using this programming paradigm, in the sense that identifying and reusing already existing/deployed services can save substantial development effort. According to the literature, identifying reusable assets (i.e., components, classes, or services) is more successful and efficient when the discovery process is domain-specific. To facilitate domain-specific service discovery, we propose a service classification approach that can categorize services to an application domain, given only the service description. To validate the accuracy of our classification approach, we have trained a machine-learning model on thousands of open-source services and tested it on 67 services developed within two companies employing servicebased software development. The study results suggest that the classification algorithm can perform adequately in a test set that does not overlap with the training set; thus, being (with some confidence) transferable to other industrial cases. Additionally, we expand the body of knowledge on software categorization by highlighting sets of domains that consist 'grey-zones' in service classification.

show abstract

“…Hybrid ML approaches that combine complementary models have been reported to have higher accuracy or a better interpretation of results than standalone models [ 23 - 25 ]. Combining supervised models such as XGBoost and logistic regression with unsupervised learning may help to overcome the challenges of predicting measles cases, based on the assumption that unsupervised learning processes will extract patterns from data that can be used as a new set of features that are less prone to biases introduced by multicollinearity and imbalanced data [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Ru¹,

Kujawski²,

Afanador³

et al. 2023

JMIR Form Res

View full text Add to dashboard Cite

Background Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources. Objective We aimed to validate and compare extreme gradient boosting (XGBoost) and logistic regression, 2 supervised learning approaches, to predict the US counties most likely to experience measles cases. We also aimed to assess the performance of hybrid versions of these models that incorporated additional predictors generated by 2 clustering algorithms, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and unsupervised random forest (uRF). Methods We constructed a supervised machine learning model based on XGBoost and unsupervised models based on HDBSCAN and uRF. The unsupervised models were used to investigate clustering patterns among counties with measles outbreaks; these clustering data were also incorporated into hybrid XGBoost models as additional input variables. The machine learning models were then compared to logistic regression models with and without input from the unsupervised models. Results Both HDBSCAN and uRF identified clusters that included a high percentage of counties with measles outbreaks. XGBoost and XGBoost hybrid models outperformed logistic regression and logistic regression hybrid models, with the area under the receiver operating curve values of 0.920-0.926 versus 0.900-0.908, the area under the precision-recall curve values of 0.522-0.532 versus 0.485-0.513, and F2 scores of 0.595-0.601 versus 0.385-0.426. Logistic regression or logistic regression hybrid models had higher sensitivity than XGBoost or XGBoost hybrid models (0.837-0.857 vs 0.704-0.735) but a lower positive predictive value (0.122-0.141 vs 0.340-0.367) and specificity (0.793-0.821 vs 0.952-0.958). The hybrid versions of the logistic regression and XGBoost models had slightly higher areas under the precision-recall curve, specificity, and positive predictive values than the respective models that did not include any unsupervised features. Conclusions XGBoost provided more accurate predictions of measles cases at the county level compared with logistic regression. The threshold of prediction in this model can be adjusted to align with each county’s resources, priorities, and risk for measles. While clustering pattern data from unsupervised machine learning approaches improved some aspects of model performance in this imbalanced data set, the optimal approach for the integration of such approaches with supervised machine learning models requires further investigation.

show abstract

A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services

Cited by 6 publications

References 12 publications

Machine learning in infectious diseases: potential applications and limitations

Machine learning in infectious diseases: potential applications and limitations

Service Classification through Machine Learning: Aiding in the Efficient Identification of Reusable Assets in Cloud Application Development

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches

Contact Info

Product

Resources

About