Pattern Classification Using Ensemble Methods
By Lior Rokach. Series in Machine Perception and Artificial Intelligence, 2009
305 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/3ee6334ccf788cab8469295a8b143390e90d247c
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
- Zeyuan Allen-Zhu
- Yuanzhi Li
- Bernardete Ribeiro
- An Chen
- Ningshan Chen
- Quanquan Gu
- Manuel Graña
- Michal Wozniak
In Computer Vision and Pattern Recognition
- Zeyuan Allen-Zhu
- Yuanzhi Li
- Manuel Graña
- Michal Wozniak
- Bernardete Ribeiro
- An Chen
- Alex Alves Freitas
- Elie Bursztein
Citations in the corpus
- A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Using a meta-learner, this method tries to induce which classifiers are reliable and which are not [190].”
- A survey of multiple classifier systems as hybrid systems (2014, background): “These reviews include the books by Kuncheva [22], Rokach [23], Seni and Edler [24], and Baruque and Corchado [25].”
- The End is Nigh: Generic Solving of Text-based CAPTCHAs (2014, background): “The Condorcet theorem [40] implies that combining paths yields better predictive performance than could be obtained from a single path in isolation.”
- Comprehensible classification models: a position paper (2014, background): “Model interpretability was stressed in early machine learning research [57]; but in the last two decades, popular classification methods like ensembles [60], support vector machines (SVM) and kernel-based learning methods [13] have been designed to maximize predictive accuracy only.”
- Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning (2020, background): “This can be viewed as a feature selection process (Alvarez et al., 2012; Cai et al., 2018; Oliveira et al., 2003; Opitz, 1999; Rokach, 2010), and we have confirmed it for NTK in practice, see Figure 1.”
- Financial credit risk assessment: a recent review (2015, background): “Many researchers have provided the supporting evidences that MCS methodology can produce better performance than individual credit risk assessment models (Deligianni and Kotsiantis 2012; Kuncheva 2004; Rokach 2010; Verikas et al. 2010; Zhou 2012).”