Ensemble Pruning Via Semi-definite Programming

By Yi Zhang, S. Burer, W. Street. Journal of machine learning research, 2006

325 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3f99c19bfd65cc16ff9518f2240b620cf0d2064f

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In Artificial Intelligence

Citations in the corpus

Ensemble learning: A survey (2018, methodology): “These methods search for a subset of ensemble members that performs at least as well as the original ensemble (Zhang, Burer, & Street, 2006).”
Mining Multi-label Data (2010, background): “Recently, the issue of learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of new applications, such as semantic annotation of images [1, 2, 3] and video [4, 5], functional genomics [6, 7, 8, 9, 10], music categorization i”
Getting the Most Out of Ensemble Selection (2006, unknown): “Note that this pruning is different from work on ensemble pruning [6, 12, 13, 14, 7].”
Scalable multi-label annotation (2014, background): “…is important in multiple domains, such as labeling actions in videos [11], news article topics [15], functional classes of genes [8], musical attributes or emotions in songs [12], semantic classes of scenes [2], product categories customers are likely to buy [21], and categories of web pages [18].”
Ensemble-based classifiers (2010, result): “The results indicate that on average ERP outperforms the other methods. Zhang et al. (2006) formulated the”
Born-Again Tree Ensembles (2020, unknown): “…1999; Caruana et al., 2004; Banﬁeld et al., 2005; Hu et al., 2007; Partalas et al., 2010; Rokach, 2009; Zhang & Wang, 2009) and search-based methods (see, e.g., Prodromidis & Stolfo, 2001; Windeatt & Ardeshir, 2001; Zhou et al., 2002; Zhou & Tang, 2003; Rokach et al., 2006; Zhang et al., 2006).”
The Interplay of Optimization and Machine Learning Research (2006, methodology): “(Zhang et al., 2006) looks at pruning an ensemble of classifiers constructed from a pool of already trained classifiers.”
Sequential Model-Based Ensemble Optimization (2014, methodology): “[2014] suggests that methods based on the covariance of the predictions such as ensemble pruning [Zhang et al., 2006] and MinCq [Roy et al.”