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CSMES (version 1.0.1)

Cost-Sensitive Multi-Criteria Ensemble Selection for Uncertain Cost Conditions

Description

Functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) ) for cost-sensitive learning under unknown cost conditions.

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Version

Install

install.packages('CSMES')

Monthly Downloads

215

Version

1.0.1

License

GPL (>= 2)

Maintainer

Koen De Bock

Last Published

February 3rd, 2023

Functions in CSMES (1.0.1)

CSMES.predictPareto

Generate predictions for all Pareto-optimal ensemble classifier candidates selected through CSMES
brierCurve

Calculates Brier Curve
plotBrierCurve

Plots Brier Curve
CSMES.ensNomCurve

CSMES Training Stage 2: Extract an ensemble nomination curve (cost curve- or Brier curve-based) from a set of Pareto-optimal ensemble classifiers
BFP

Business failure prediction demonstration data set
CSMES.ensSel

CSMES Training Stage 1: Cost-Sensitive Multicriteria Ensemble Selection resulting in a Pareto frontier of candidate ensemble classifiers
CSMES.predict

CSMES scoring: generate predictions for the optimal ensemble classifier according to CSMES in function of cost information.