Dyad Ranking Using A Bilinear Plackett-Luce Model

Schäfer, Dirk; Hüllermeier, Eyke

doi:10.1007/978-3-319-23525-7_14

Cited by 19 publications

(14 citation statements)

References 21 publications

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“…For future work, we are planning to generalize our methodology by exploiting properties (features) of veri cation tools, which are only identi ed by their name so far. Recently, a generalization of label ranking called dyad ranking has been proposed, in which not only the instances but also the alternatives to be ranked can be described in terms of properties [18]. As an important advantage of this approach, note that it in principle allows for ranking alternatives with very few or even no training information so far.…”

Section: Resultsmentioning

confidence: 99%

Predicting rankings of software verification tools

Czech

Hüllermeier

Jakobs

et al. 2017

Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics

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So ware veri cation competitions, such as the annual SV-COMP, evaluate so ware veri cation tools with respect to their e ectivity and e ciency. Typically, the outcome of a competition is a (possibly category-speci c) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of veri cation tools on a given veri cation task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on veri cation tasks. e method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for veri cation tasks. Our kernels employ a graph representation for so ware source code that mixes elements of control ow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). CCS CONCEPTS•Computing methodologies →Ranking; Support vector machines; Cross-validation; •So ware and its engineering →So ware veri cation; Formal so ware veri cation; KEYWORDSSo ware veri cation, machine learning, ranking.

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Section: Resultsmentioning

confidence: 99%

Predicting rankings of software verification tools

Czech

Hüllermeier

Jakobs

et al. 2017

Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics

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“…Applications of that kind lead to more complex multi-target prediction problems that are often referred to as dyadic prediction, link prediction, or network inference settings-see e.g. (Menon and Elkan, 2010;Schäfer and Hüllermeier, 2015). In this area, one can distinguish algorithms that model vector representations or structured target representations, as well as methods that model target relations.…”

Section: Problems That Involve Side Information For Targetsmentioning

confidence: 99%

Multi-target prediction: a unifying view on problems and methods

Waegeman

Dembczyński

Hüllermeier

2018

Data Min Knowl Disc

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Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.

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“…Due to the practical significance, label ranking has attracted increasing attention in the recent machine learning literature, and a large number of methods have been proposed or adapted for label ranking [5][6][7][8][9][10][11][12][13][14][15]. An overview of label ranking algorithms can be found in [16,17].…”

Section: Related Workmentioning

confidence: 99%

Random forest for label ranking

Zhou

Qiu

2018

Expert Systems with Applications

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Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.

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Dyad Ranking Using A Bilinear Plackett-Luce Model

Cited by 19 publications

References 21 publications

Predicting rankings of software verification tools

Predicting rankings of software verification tools

Multi-target prediction: a unifying view on problems and methods

Random forest for label ranking

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