Decision trees for hierarchical multi-label classification

By Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski, Hendrik Blockeel. 2008

718 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Feature selection for multi-label naive Bayes classification (2009, background): “Recently, several algorithms have also been proposed to improve the performance of learning systems through exploring additional information provided by the hierarchical structure of the classes [5,32,42] or unlabeled data [7,24].”
On the Stratification of Multi-label Data (2011, methodology): “For example, the stratified sampling approaches discussed in this paper could be used for reduced error pruning of multi-label decision trees [24], for down-sampling without replacement in the ensembles of pruned sets approach [17] and for deciding when to stop the training of a multi-label neural n”
Multilabel relationship learning (2013, positive): “Prior knowledge about relationships between labels may exist in some applications, and hierarchical multilabel learning methods [Cesa-Bianchi et al. 2006; Rousu et al. 2006; Vens et al. 2008; Hariharan et al. 2010] can utilize such prior knowledge.”
An extensive experimental comparison of methods for multi-label learning (2012, background): “One evaluation measure that can be easily adapted for multi-label setting is the precision-recall curve (and the area under the precision-recall curve thereof) [47].”
Combining instance-based learning and logistic regression for multilabel classification (2009, methodology): “…well-established methods for conventional classiﬁ-cation have been extended to the multi-label case, including support vector machines [Godbole and Sarawagi, 2004; Elisseeff and Weston, 2002; Boutell et al. , 2004], neural networks [Zhang and Zhou, 2006], and decision trees [Vens et al. , 2008].”
Hierarchical Multi-Label Classification Networks (2018, neutral): “We compare HMCN-(F/R) with four HMC algorithms which are considered the state-of-the-art for HMC: ClusHMC (Vens et al., 2008), Clus-HMC-Ens (Schietgat et al., 2010), CSSA (Bi & Kwok, 2011), and HMC-LMLP (Cerri et al., 2016).”
Efficient Multi-label Classification with Many Labels (2013, methodology): “For performance evaluation, we will use two popular measures: (i) the RMSE as defined in (6); and (ii) the micro-averaged area under the precision-recall curve (AUPRC) (Vens et al., 2008).”
Mining Multi-label Data (2010, background): “An extension of the work in [8] for handling DAG label structures is presented in [58].”
Multi-target regression via input space expansion: treating targets as inputs (2012, neutral): “It is interesting to note that the predictive clustering framework is an example of a technique that originally focused on multi-target regression, and only recently showcased its effectiveness on multi-label classification (Vens et al., 2008; Madjarov et al., 2012).”
Kernel Methods and Machine Learning (2014, neutral): “5 [40], multi-label decision trees [284], and AdaBoost.”
A Tutorial on Multilabel Learning (2015, methodology): “Decision tree methods have been mainly used in the field of genomics due to the interpretability of its outputs and in hierarchical [Blockeel et al. 2006; Vens et al. 2008] and ensemble settings [Madjarov et al.”
Multi-label Classification Using Ensembles of Pruned Sets (2008, methodology): “Essentially, these modifications simply employ some form of PT method internally and can often be generalised to any single-label classifier.”
Multilabel Classification with Principal Label Space Transformation (2012, background): “Because of the wide range of potential applications in genomics (Barutcuoglu et al., 2006; Vens et al., 2008), scene classification (Boutell et al.”
Machine learning for discovering missing or wrong protein function annotations (2019, methodology): “Almeida and Borges [26] proposed an adaptation of K-Nearest Neigh-bours to address quantification learning in HMC. Similarly, Triguero and Vens [27] investigated how different thresholds can increase the performance of Predictive Clustering Trees in this context.”
Classifier chains for multi-label classification (2009, methodology): “Well-known approaches include AdaBoost (Schapire and Singer 2000), decision trees (Vens et al. 2008), and lazy methods (Zhang and Zhou 2007).”