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Past Sessions > Program 2020

IncrLearn

Incremental classification and clustering, concept drift, novelty detection in big/fast data context

In conjunction with

20th IEEE International Conference on Data Mining

November 17-20, 2020 ,  Sorrento, Italy

 

 
15H-16H00 (UTC): Plenary speech

Manuel Roveri: "Is Tiny Deep Learning the new Deep Learning?”

 
 
16H00-17H00 (UTC): Session 1

16H00-16H15 (UTC): Alessio Bernardo, Emanuele Della Valle, and Albert Bifet,

“Incremental Rebalancing Learning on Evolving Data Streams”

 

16H15-16H30 (UTC): Giacomo Ziffer, Emanuele Della Valle, Alessio Bernardo, and Albert Bifet,

“Fast Incremental Naïve Bayes with Kalman Filtering”

 

16H30-16H45 (UTC): Alexandre Abraham and Léo Dreyfus-Schmidt,

“Rebuilding Trust in Active Learning with Actionable Metrics”

 

16H45-17H00 (UTC): Parsa Vafaie, Herna Viktor, and Wojtek Michalowski,

“Multi-class imbalanced semi-supervised learning from streams through online ensembles”

 
 
17H00 -17H15 (UTC): Break
 
 
17H00-18H00 (UTC): Session 2

17H15-17H30 (UTC): Shujie Yin, Guanjun Liu, Zhenchuan Li, Chungang Yan, and Changjun Jiang,

“An Accuracy-and-Diversity-based Ensemble Method for Concept Drift and Its application in Fraud Detection”

 

17H30-17H45 (UTC): Zoltan Puha, Maurits Kaptein, and Aurelie Lemmens,

"Batch Mode Active Learning for Individual Treatment Effect Estimation"

 

17H45-18H00 (UTC): Tengyue Li, Simon Fong, Yaoyang Wu, and Antonio J. Tallón Ballesteros,

"Kennard-Stone Balance Algorithm for Time-series Big Data Stream Mining"

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