title: Binary classification of imbalanced datasets using conformal prediction.
abstract: Aggregated Conformal Prediction is used as an effective alternative to other, more complicated and/or ambiguous methods involving various balancing measures when modelling severely imbalanced datasets. Additional explicit balancing measures other than those already apart of the Conformal Prediction framework are shown not to be required. The Aggregated Conformal Prediction procedure appears to be a promising approach for severely imbalanced datasets in order to retrieve a large majority of active minority class compounds while avoiding information loss or distortion.
authors.affiliation: Swedish Toxicology Sciences Research Center, SE-151 36 Södertälje, Sweden. Electronic address: ulf.norinder@swetox.se.
authors.forename: Ulf
authors.initials: U
authors.lastname: Norinder
authors.affiliation: Swedish Toxicology Sciences Research Center, SE-151 36 Södertälje, Sweden. Electronic address: scott.boyer@swetox.se.
authors.forename: Scott
authors.initials: S
authors.lastname: Boyer
mesh_terms: D062126:Databases, Chemical; D008968:Molecular Conformation; D060388:Support Vector Machine
keywords: Aggregated conformal prediction; Imbalanced datasets; QSAR; Signature descriptors; Support vector machines