Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

By Robert K. Bryll, Ricardo Gutierrez-Osuna, Francis K. H. Quek. Pattern Recognition, 2003

486 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/07b27e79099f00a8d50f9e529e6b325ed827ead2

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Citations in the corpus

Ensemble Classifiers for Steganalysis of Digital Media (2012, background): “Decision forests [24], attribute bagging [9], Classification by Ensembles from Random Partitions (CERP) [1], or the recently proposed Random Subsample Ensemble (RSE) [47] are all ensemble-based classifiers sampling the feature space prior base learner training to either increase the diversity among ”
A survey of multiple classifier systems as hybrid systems (2014, methodology): “Bagging applies sampling with replacement to obtain independent training datasets for each individual classifier.”
Feature Noising for Log-Linear Structured Prediction (2013, methodology): “Dropout is is similar in spirit to feature bag-ging in the deliberate removal of features, but performs the removal in a preset way rather than randomly (Bryll et al., 2003; Sutton et al., 2005; Smith et al., 2005).”
Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data (2010, methodology): “In regard to these findings and the fact that ensembles generated by RFS can outperform bagging and boosting in terms of accuracy [21], [32], the construction of SVM ensembles by RFS seems more adequate.”
Ensemble learning: A survey (2018, methodology): “Bryll, Gutierrez-Osuna, and Quek (2003) introduced attribute bagging (AB), an approach in which an inducer is trained using a randomly selected subset of features.”
Getting the Most Out of Ensemble Selection (2006, methodology): “[5] and used in random forests [4].”
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning (2020, background): “Ensemble works for random features / NTK. Traditional theorems (Alhamdoosh & Wang, 2014; Brown et al., 2005a; Bryll et al., 2003; Tsymbal et al., 2005) suggest that the ensemble of independently trained random feature models can indeed significantly improve test-time performance, as it enlarges the…”