Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples

By Ming Li, Zhi-Hua Zhou. 2007

382 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “cation and regression models are employed to analyze current and historical trends to make predictions in ﬁnancial markets [24, 33, 8], to study biological problems [54], to support medical diagnosis [30, 42, 57], to classify network traﬃc or detect anomalies [22, 28, 39, 12, 49]. One may think that”
Combining Committee-Based Semi-Supervised Learning and Active Learning (2010, methodology): “Li and Zhou[2] proposed an extension to TriTraining, called Co-Forest, in which an initial ensemble of random trees is trained on bootstrap subsamples generated from the given labeled dataset L.”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, methodology): “Standard self-training Self-Training [13] Standard co-training Co-Training [15] Statistical co-learning Statistical-Co [34] ASSEMBLE ASSEMBLE [36] Democratic co-learning Democratic-Co [37] Self-training with editing SETRED [14] Tri-training TriTraining [20] Tri-training with editing DE-TriTraining [”
Deep forest (2017, background): “This also offers gcForest with the opportunity of incorporating active learning [Freund et al. , 1997; Huang et al. , 2014] and/or semi-supervised learning strategies [Zhou and Li, 2010; Li and Zhou, 2007].”
A survey on semi-supervised learning (2019, background): “The authors of the tri-training approach proposed to extend it tomore than three learners— notably, to random forests (Li and Zhou 2007).”
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification (2014, background): “Multiview learning [16] [17] can be viewed as a generalization of cotraining , without requiring explicit feature splits or the iterative mutual-teaching procedure.”