New POSSE-kNN method improves binary classification accuracy

A new machine learning method called POSSE-kNN has been developed for binary classification tasks, particularly for tabular data. This ensemble technique combines bootstrap sampling, random feature subspaces, out-of-bag screening, and a novel pathwise selection of neighbors. Evaluations on ten benchmark datasets show POSSE-kNN achieving superior aggregate results in accuracy, Cohen's kappa, and Brier score compared to six established methods. AI

IMPACT Introduces a novel ensemble method that enhances performance on tabular data classification tasks.

RANK_REASON The cluster contains a research paper detailing a new machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• Amjad Ali
• arXiv
• binary classification
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• k-nearest neighbors algorithm
• POSSE-kNN
• ScienceCast
• support vector machine
• tabular data

• paper
• other

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