Prediction of wart treatment response using a hybrid GA-ensemble learning approach

Qasem, Ahmed Gailan; Lam, Sarah S.

doi:10.1016/j.eswa.2023.119737

Cited by 10 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marcelo N. Kapp et al proposed a new method for optimizing support vector machine parameters, which dynamically selects the optimal SVM model. Through testing on 14 synthetic and real datasets, the results show that the dynamically optimized SVM model is very effective in completely dynamic environments [39]. Genetic algorithms have also played a great role in the medical field.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Reports Classification Based on Semi-Supervised Learning Methods

Feng¹,

Han²,

Chen³

2023

Preprint

View full text Add to dashboard Cite

Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China (PBC). Since there are no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then Construct ant colony search to optimize intelligent recognition. Finally, this study uses SVM, BP neural network, and random forest to classify users and compare their results. The research re-sults indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

show abstract

Section: Literature Reviewmentioning

confidence: 99%