14 results for “Ensemble Methods Classifier”

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Research ArticleOpen accessIn the abstractImpact of an Extra Layer on the Stacking Algorithm for Classification ProblemsRodolfo Lorbieski, Silvia Modesto NassarClassifying and making decisions are tasks performed by any human being in their daily lives. Learning algorithms have been widely studied as tools to aid information management, with an objective to maximize the generalization capacity. Learning algorithms can be used individually or as a committee of machines (ensembles). An ensemble uses the solutions provided by several machines, making different combinations … …Journal of Computer Science, Vol. 14, Issue 5 · 10 Jan 2018 · 8,286 accessesView PDF Cite

Research ArticleOpen accessIn the abstractEnhanced Ensemble Prediction Algorithms for Detecting Faulty Modules in Object Oriented Systems Using Quality MetricsM. Punithavalli, C. NeelamegamThe high usage of software system poses high quality demand from users, which results in increased software complexity. To address these complexities, software quality engineering methods should be updated accordingly and enhance their quality assuring methods. Fault prediction, a sub-task of SQE, is designed to solve this issue and provide a strategy to identify faulty parts of a program, so … …Journal of Computer Science, Vol. 8, Issue 12 · 20 Dec 2012 · 6,435 accessesView PDF Cite

Research ArticleOpen accessIn the abstractBoosting Kernel Discriminative Common Vectors for Face RecognitionC. Lakshmi, M. PonnavaikkoProblem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 6,571 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordImproving Diabetes Risk Prediction Using Ensemble Boosting and SMOTE-Based Class BalancingKittipol Wisaeng, Pankom Sriboonlue, Benchalak MuangmeesriAccurate diabetes prediction is vital for early intervention, optimized resource allocation, and minimizing long-term complications. This study presents a comparative evaluation of traditional and advanced machine learning models for diabetes classification using a structured clinical dataset. Seven baseline algorithms were assessed against five advanced ensemble methods: CatBoost, LightGBM, XGBoost, Voting Ensemble, and Stacking Ensemble. To improve algorithm learning, the Synthetic … …Journal of Computer Science, Vol. 22, Issue 1 · 1 Feb 2026 · 1,976 accessesView PDF Cite