A label ranking approach for selecting rankings of collaborative filtering algorithms

Cunha, Tiago; Soares, Carlos; Carvalho, André C. P. L. F. de

doi:10.1145/3167132.3167418

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…As another example, in image recognition, objects are ordered according to their importance in the image [4], [5]. In meta-learning domains, the characteristics of each problem instance are considered, and the output is an ordered list of algorithms according to their suitability to the given problem [6], [7]. Lastly, In text classification, a label ranking algorithm can be employed to output a ranked list of topics, tags or advertisements for a document or web page (the instance) [8], [9].…”

Section: Introductionmentioning

confidence: 99%

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Dery

Shmueli

2020

IEEE Access

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Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. Herein, we present a novel boosting algorithm, BoostLR, that was specifically designed for label ranking tasks. Similarly to other boosting algorithms, BoostLR, proceeds in rounds, where in each round, a single weak model is trained over a sampled set of instances. Instances that were identified as harder to predict in the current round, receive a higher (boosted) weight, and therefore also a higher probability to be included in the sample of the forthcoming round. Extensive evaluation of our proposed algorithm on 24 semi-synthetic and real-world label ranking datasets concludes that our algorithm significantly outperforms the current state-of-the-art label ranking methods.

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Section: Introductionmentioning

confidence: 99%

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Dery

Shmueli

2020

IEEE Access

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“…The set of candidate algorithms A is defined by sampling up to 100 different parameterizations of 18 classification algorithms stemming from the Java machine learning library WEKA [Frank et al, 2016], ensuring these parameterizations not being too similar. An overview of the algorithms, their parameters and the number of instantiations contained in A is given in Table 1.…”

Section: Benchmark Datasetmentioning

confidence: 99%

“…In [Cunha et al, 2018], a label-ranking-based AS approach for selecting collaborative filtering algorithms in the context of recommender systems is presented, and various label ranking algorithms are compared, including nearest neighbor and random forest label rankers. Similarly, [Kanda et al, 2012] use a multi-layer perceptron powered label ranker to select meta-heuristics for solving TSP instances.…”

Section: Related Workmentioning

confidence: 99%

“…Assuming a complete performance matrix for training, low-rank latent factors are learned in [Malitsky and O'Sullivan, 2014] using singular value decomposition to obtain a selector en par with the oracle. Lastly, [Fusi et al, 2018] demonstrates how probabilistic matrix factorization can be used to build and select machine learning pipelines.…”

Section: Related Workmentioning

confidence: 99%

“…On the contrary, especially in the field of automated machine learning (AutoML), combined algorithms selection and hyperparameter optimization problems are considered, where the number of potential candidates is po- * Contact Author tentially infinite [Thornton et al, 2013;Feurer et al, 2015;Mohr et al, 2018]. However, these methods heavily rely on computationally extensive search procedures combined with costly online evaluations of the performance measure to optimize for, since learning effective meta models for an instantaneous recommendation becomes infeasible.…”

Section: Introductionmentioning

confidence: 99%

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Extreme Algorithm Selection With Dyadic Feature Representation

Tornede,

Wever,

Hüllermeier

2020

Preprint

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Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in combined algorithm selection and hyperparameter optimization problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. Therefore, here we propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms, facilitating meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described. We find the latter to improve significantly over the current state of the art in various metrics.

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