Assessing Batch and Online Learning for Delivery in Full and On Time Predictions-Reference-Cited by-同舟云学术

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Assessing Batch and Online Learning for Delivery in Full and On Time Predictions

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 International Joint Conference on Neural Networks (IJCNN)
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Author:

de Lima Adriano Alves¹,Batista Marcie Venancio¹,Barddal Jean Paul²,Sanches Danilo Sipoli³,de Oliveira Luiz Eduardo Soares¹

Affiliation:

1. Federal University of Paraná,Department of Informatics,Curitiba,Brazil

2. Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná,Curitiba,Brazil

3. Federal University of Technology of Paraná Cornélio,Department of Computer Science,Procópio,Brazil

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9891857/9889787/09892386.pdf?arnumber=9892386

Reference32 articles.

1. Understanding variable importances in forests of randomized trees;louppe;Advances in neural information processing systems,2013

2. Kappa Updated Ensemble for drifting data stream mining

3. Cost-sensitive learning for imbalanced data streams

4. A survey on data preprocessing for data stream mining: Current status and future directions

5. Lessons learned from data stream classification applied to credit scoring

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