Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem
Ioannis Markoulidakis, George Kopsiaftis, Ioannis Rallis, Ioannis Georgoulas
Abstract
The paper presents a novel method for reducing a multi-class Confusion Matrix into a 2 × 2 version enabling the use of the relevant performance metrics and methods like the Receiver Operator Characteristic and the Area Under the Curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a specific Confusion Matrix type. The developed method is then exploited for the assessment of several state-of-the-art machine learning algorithms applied on a customer experience metric.
Topics & Concepts
Confusion matrixConfusionMetric (unit)Class (philosophy)Reduction (mathematics)Computer scienceMatrix (chemical analysis)Artificial intelligenceOperator (biology)Machine learningAlgorithmPattern recognition (psychology)MathematicsEngineeringRepressorTranscription factorBiochemistryComposite materialGeometryGenePsychoanalysisChemistryPsychologyMaterials scienceOperations managementImbalanced Data Classification TechniquesFace and Expression RecognitionRough Sets and Fuzzy Logic