Stacked Generalization

2017

1,897 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Optimization & Training

Citations in the corpus

• Deep learning in neural networks: An overview (2014, neutral): “Compare Stacked Generalization (Ting &Witten, 1997; Wolpert, 1992), and FNNs that profit from pre-training by competitive UL (e.”
• DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, methodology): “As the trained fusion rules require a separate training set for the base classifiers and the combiner (to avoid over-fitting), we run a two-level (nested) cross-validation procedure, usually referred to as stacked generalization [41].”
• Stacking with Auxiliary Features for Visual Question Answering (2018, background): “(Wolpert, 1992) trains a supervised meta-classifier to appropriately combine multiple system outputs.”
• Memory Efficient Experience Replay for Streaming Learning (2018, neutral): “[37], [38], [39], [56], [63] or ensemble methods [7], [9], [27], [44], [46], [51], [54], [61], [70], [72].”
• A Review of Relational Machine Learning for Knowledge Graphs (2015, neutral): “This is called stacking [106].”
• Contextualizing Object Detection and Classification (2015, background): “Only recently, object hierarchy context has drawn much research attention [8], [47].”
• Big data for cyber physical systems in industry 4.0: a survey (2019, neutral): “The four typical methods are bagging (Breiman 1996), boosting (Schapire 1990), and stacked generalization (Wolpert 1992).”
• Remarks on multi-output Gaussian process regression (2018, methodology): “Stacked single-target The SST is developed from the stacked generalization idea [65] in multilabel classification.”
• A survey on multi‐output regression (2015, methodology): “The multi-target regressor stacking (MTRS) method 4 is inspired by 29 where stacked generalization 30 was used to deal with multi-label classification.”
• A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects (2022, methodology): “It was 717 introduced in 1992 by Wolpert [117] to reduce the generaliza-718 tion error in machine learning problems.”
• Handbook of Big Data (2016, methodology): “Subsemble is a form of supervised stacking [2,15] and is similar in nature to the Super Learner algorithm, with the distinction that base learner fits are trained on subsets of the data instead of the full training set.”
• Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020, background): “Stacking al-gorithm employs a two ‐ staged structure to combine the predictions of multiple learning algorithms [72].”
• Efficient 2D and 3D Facade Segmentation Using Auto-Context (2016, positive): “We train the sequence of classifiers using stacked generalization [7].”
• Building Better Open-Source Tools to Support Fairness in Automated Scoring (2017, background): “One way to increase the interpretability of such models is to group multiple features by feature type (e.g. “syntactic relationships”) and build a stacked model (Wolpert, 1992) containing simpler models for each feature type.”
• Using Explanations to Improve Ensembling of Visual Question Answering Systems (2017, methodology): “Traditional stacking [Wolpert, 1992] trains a supervised meta-classifier to appropriately combine multiple system outputs.”
• Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, methodology): “One particularly interesting approach would be to use Stacking (Wolpert, 1992) to combine all the individually learned models into a larger model that exploits the varying quantitative predictions of the different base-learner and molecular representation combinations.”
• Auto-sklearn: Efficient and Robust Automated Machine Learning (2019, methodology): “We experimented with different approaches to optimize these weights: stacking [37], gradient-free numerical optimization, and the method ensemble selection [9].”