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Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets

https://doi.org/10.1016/j.ins.2017.09.013

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What this work says

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IMPROVING PERFORMANCE FOR IMBALANCED DATA CLASSIFICATION USING OVERSAMPLING AND CHARACTERISTICS OF EACH CLUSTER

inferred← citeshttps://doi.org/10.70675/e06201a6zf272z42c0z8b3czbf0ea31444d1openalex-snapshot

An enhanced multi-protocol middleware solution for Internet of things

inferred← citeshttps://doi.org/10.7287/peerj-cs.424v0.1/reviews/1openalex-snapshot

Peer Review #1 of "Entropy based C4.5-SHO algorithm with information gain optimization in data mining (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.424v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Entropy based C4.5-SHO algorithm with information gain optimization in data mining (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj-cs.424v0.2/reviews/2openalex-snapshot

Peer Review #2 of "Entropy based C4.5-SHO algorithm with information gain optimization in data mining (v0.2)"

inferred← citeshttps://doi.org/10.7717/peerj-cs.424openalex-snapshot

Entropy based C4.5-SHO algorithm with information gain optimization in data mining

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.