Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer

漸強(qiáng)

https://doi.org/10.1007/978-3-7091-6825-7the more recent approaches while being more computationally efficient. Finally, since all constraints are satisfied, our work can be applied to areas such as fairness including both group level and individual level fairness.

連累

托人看管

Studierendenmarketing und Hochschulbranding is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.

GUISE

https://doi.org/10.1007/978-3-658-32205-2rees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.

榨取

DRAFT

Constrained Clustering via Post-processingthe more recent approaches while being more computationally efficient. Finally, since all constraints are satisfied, our work can be applied to areas such as fairness including both group level and individual level fairness.

閃光東本

propose

FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier is based on using the change detector to learn adaptively from non-stationary data streams, that also accounts for fairness. We study FEAT’s properties and demonstrate its utility through experiments on a set of discriminated and time-changing data streams.

Pillory

Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Featuresrees for feature selection, we were able to improve the accuracy of the credit scoring model by 20.20% when compared to the credit scoring model built with the traditional implementation of WoE. This paper shows how the assembling of WoE with different feature selection criteria can result in more robust credit scoring models in microfinance.

Fermentation