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Correcting misclassification bias in regression models with variables generated via data mining

Qiao, Mengke; Huang, Ke-Wei - In: Information systems research : ISR 32 (2021) 2, pp. 462-480

Persistent link: https://www.econbiz.de/10012599859

Functional-bandwidth kernel for Support Vector Machine with Functional Data : an alternating optimization algorithm

Blanquero, R.; Carrizosa, Emilio; Jiménez-Cordero, A.; … - In: European journal of operational research : EJOR 275 (2019) 1, pp. 195-207

Persistent link: https://www.econbiz.de/10011993243

Econometric count data forecasting and data mining (cluster analysis) applied to stochastic demand in truckload routing

Miori, Virginia M. - In: Advances in Business and Management Forecasting : Vol. 6, (pp. 191-216). 2009

The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution...

Persistent link: https://www.econbiz.de/10015382165

On the accuracy of estimators based on a binary classifier

Scholtus, Sander; Delden, Arnout van - 2020

Persistent link: https://www.econbiz.de/10012174823

Implementing a new method for discriminant analysis when group covariance matrices are nearly singular

Theis, Winfried; Röver, Christian; Pouwels, Britta - 2002

We consider a unified description of classification rules for nearly singular covariance matrices. When the covariance … matrices of the groups or the pooled covariance matrix become nearly singular, bayesian classification rules become seriously … al. (1986) to optimize the classification error directly. …

Persistent link: https://www.econbiz.de/10009770527