Combining instance-based learning and logistic regression for multilabel classification

By Weiwei Cheng, Eyke Hüllermeier. Machine-mediated learning, 2009

470 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6643ec56779e4b56391603fba3db52bc085b311e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Label Distribution Learning (2013, background): “For exampl e, it can be extended fromk-NN [37], [5], decision tree [6], [24], or neural networks [36], [27].”
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “We compared our method with ML-KNN (Zhang and Zhou, 2007), BR-KNN (Spyromitros et al., 2008), IBLR-ML (Cheng and Hüllermeier, 2009), GM (Zou and Xiao, 2016), and SVM-NN (Amidi et al., 2017).”
Text Classification Algorithms: A Survey (2019, background): “w0 = log(p0)− log(1− p0) (84) To obey the basic principle underlying instance-based learning (IBL) [121], the classifier should be a function of the distance δi.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “Regarding the MLC algorithms, the following were chosen: CLR [37], RAkEL [34], IBLR [38], and HOMER [35].”
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains (2010, methodology): “…clear that several MLC methods, including those based on the idea of stacking (Godbole & Sarawagi, 2004; Cheng & Hüllermeier, 2009), seek to exploit unconditional label dependence, and so do related methods from other fields, like multivariate regression in statistics (Breiman & Friedman, 1997).”
Multi-label learning by exploiting label dependency (2010, background): “• High-order approaches: The task of multi-label learning is tackled by considering the high-order relations between labels, such as the full-order style of imposing all other labels’ influences on each label in an indirect manner [3, 10, 11, 25], or the random style of combining an ensemble of clas”
Multi-target regression via input space expansion: treating targets as inputs (2012, methodology): “Both methods have been very successful in the MLC domain and provided inspiration for many subsequent works (Cheng and Hüllermeier 2009; Dembczynski et al 2010; Kumar et al 2012; Read et al 2014).”
Scalable and efficient multi-label classification for evolving data streams (2012, methodology): “Cheng and Hüllermeier 2009; Godbole and Sarawagi 2004; Read et al. 2009). BR can be applied directly to data streams by using incremental binary base models. An alternative paradigm to BR is the label combination or label powerset method (LC). LC transforms a multi-label problem into a single-label ”
On label dependence and loss minimization in multi-label classification (2012, background): “One can see that Stacking improves over BR in both cases, but the improvement is higher when the structural parts of the model are identical.”
Classifier chains for multi-label classification (2009, methodology): “The authors of Cheng and Hüllermeier (2009) expanded on the work on lazy classification and introduced the IBLR algorithm: a combination of instance-based learning and logistic regression. IBLR takes label correlations into account by using the labels of neighboring examples as extra attributes in a”
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015, methodology): “In this section, the several published ways to accomplish classification with imbalanced MLDs are depicted, organized according to three well-known approaches: algorithmic adaptations, ensemble-based methods and resampling techniques.”