Cascade Generalization

By João Gama, Pavel Brazdil. Machine-mediated learning, 2000

300 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/80ffabd5369848a1c5beaa91bf80af5d125e22b9

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Citations in the corpus

Discriminative Input Stream Combination for Conditional Random Field Phone Recognition (2009, methodology): “Connecting classifiers in this way is in the tradition of procedures such as the Cascade Generalization of Gama and Brazdil [15], wherein the output of each successive classifier is appended to the original data to use as input to the next classifier.”
A Survey on Ensemble Learning for Data Stream Classification (2017, background): “Cascading includes architectures in which the output of one classiﬁer is the input of another for multiple levels [Alpaydin and Kaynak 1998; Gama and Brazdil 2000].”
Functional Trees (2001, methodology): “Also, Gama and Brazdil (2000) have presented Cascade Generalization, a method to combine classification algorithms by means of constructive induction.”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “Methods like base classifier selection and cascade-based classifiers are also widely used [19] [20].”
Activity Recognition from Accelerometer Data (2005, background): “Meta-level classifiers can be clustered into three frameworks: voting (used in bagging and boosting), stacking (Wolpert 1992; Dzeroski & Zenko 2004) and cascading (Gama & Brazdil 2000).”
Scalable-effort classifiers for energy-efficient machine learning (2015, methodology): “The closest related approach is the method of cascaded classi-ﬁcation [12]; the Viola-Jones algorithm used for detection is a classic example [13].”
Machine learning: a review of classification and combining techniques (2006, background): “Cascade Generalization uses the set of classifiers sequentially, at each step performing an extension of the original data by the insertion of new attributes.”
Cross-disciplinary perspectives on meta-learning for algorithm selection (2009, methodology): “2005]; combining models via stacked generalization [Wolpert 1992; Chan and Stolfo 1997], ensembles [Opitz and Maclin 1999] and other approaches [Gama and Brazdil 2000; Todorovski and Dzeroski 2003; Peterson and Martinez 2005]; meta-learning of broader data mining processes that include algorithm sel”
Using Meta-Learning to Support Data Mining (2004, background): “Research in this area investigates what base-learners and meta-learners produce best empirical results (e.g., [20],[26]); how to represent class predictions (class labels versus class-posterior probabilities [48]); and how to define meta-features ([3],[15]).”