Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes

By Michael D. Muhlbaier, Apostolos Topalis, Robi Polikar. 2009

226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Meta & Continual Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jerzy Stefanowski
2. Ali Ayub
3. Alan R. Wagner
4. João Gama
5. Albert Bifet
6. Heitor Murilo Gomes
7. Fabrício Enembreck
8. Jean Paul Barddal

In Meta & Continual Learning

1. Ali Ayub
2. Alan R. Wagner
3. Ryan Elwell
4. Yang Yu
5. Zhi-Hua Zhou
6. Qing Da
7. Albert Bifet
8. Heitor Murilo Gomes

Citations in the corpus

• Recent Advances in Open Set Recognition: A Survey (2018, background): “On the other hand, though new classes (UUCs) are assumed to appear incremental in class incremental learning (C-IL) [125], [126], [127], [128], [129], [130], these studies mainly focused on how to enable the system to incorporate later coming training samples from new classes instead of handling the”
• Learning with Augmented Class by Exploiting Unlabeled Data (2014, methodology): “…Polikar et al. 2001; Fern and Givan 2003), the attribute-incremental learning (A-IL) (Vapnik, Vashist, and Pavlovitch 2009), the class-incremental learning (C-IL) (Fink et al. 2006; Muhlbaier, Topalis, and Polikar 2009; Kuzborskij, Orabona, and Caputo 2013) as concluded in (Zhou and Chen 2002).”
• A Survey on Ensemble Learning for Data Stream Classification (2017, methodology): “NC algorithm [Muhlbaier et al. 2009] uses a voting method, namely the dynamically weighted consult and vote (DW-CAV), in which classifiers consult its peer decisions to check if its decision is aligned with them and with the classes it was trained on.”
• Ensemble learning for data stream analysis: A survey (2017, background): “NC [130] Learn ++ ensemble for novel class detection”
• Cognitively-Inspired Model for Incremental Learning Using a Few Examples (2020, background): “Most of the earliest approaches did not fulfill the criteria for class-incremental learning and many required old class data to be available when learning new classes: [25, 36, 37, 35].”
• A survey on ensemble learning (2019, background): “[65] designed a dynamically weighted consult and a voting mechanism to integrate classifiers for the incremental learning of new new class.”
• Incremental Learning of Concept Drift in Nonstationary Environments (2011, methodology): “Combining schema and scaffolding theories, Kuncheva’s suggested desiderata for learning concept drift [15], and the generally accepted definitions of incremental learning [16], [17], we use the following guidelines to develop a framework for learning in non-stationary environments.”
• Brain-Inspired Model for Incremental Learning Using a Few Examples (2020, background): “Most of the earliest approaches did not fulfill the criteria for class-incremental learning and many required old class data to be available when learning new classes: [23, 33, 34, 32].”