|
|
|
Past Sessions > Program 2020IncrLearnIncremental 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?” 16H00-16H15 (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” 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" |
