Boosting methods for multi-class imbalanced data classification: an experimental review
Jafar Tanha; Yousef Abdi; Negin Samadi; Nazila Razzaghi; Mohammad Asadpour · 2020 · Journal Of Big Data
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets. For this reason, researchers have been paid attention and have proposed many methods to deal with this problem, which can be broadly categorized into data level and algorithm level. Besides, multi-class imbalanced learning is much harder than binary one and is still an open problem. Boosting algorithms are a class of ensemble learning methods in
Abstract by Jafar Tanha; Yousef Abdi; Negin Samadi; Nazila Razzaghi; Mohammad Asadpour, Journal Of Big Data (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s40537-020-00349-y