Learning rules for multi-label classification: a stacking and a separate-and-conquer approach

Mencía, Eneldo Loza; Janssen, Frederik

doi:10.1007/s10994-016-5552-1

Cited by 31 publications

(32 citation statements)

References 44 publications

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“…Future studies will further explore labels' dependencies (Loza Mencía & Janssen, 2016;Papagiannopoulou, Tsoumakas, & Tsamardinos, 2015), particularly, by using Bayesian networks to model it beforehand (Wang, Wang, Wang, & Ji, 2014) and also through specific recommender algorithms like those based on collaborative filtering (Adomavicius & Tuzhilin, 2005). Additionally, the authors plan to further investigate the prediction of labels' problems and assess the proposed ESL strategy in other MLC domains.…”

Section: Discussionmentioning

confidence: 99%

Enhancing multilabel classification for food truck recommendation

2018

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Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.

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Section: Discussionmentioning

confidence: 99%

Enhancing multilabel classification for food truck recommendation

2018

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“…By using a separate-and-conquer strategy the step of inducing descriptive but often redundant models of the data is omitted. Instead, classification rules that aimed at providing accurate predictions are learned directly [9].…”

Section: Related Workmentioning

confidence: 99%

Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning

2019

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Being able to model correlations between labels is considered crucial in multi-label classification. Rule-based models enable to expose such dependencies, e.g., implications, subsumptions, or exclusions, in an interpretable and human-comprehensible manner. Albeit the number of possible label combinations increases exponentially with the number of available labels, it has been shown that rules with multiple labels in their heads, which are a natural form to model local label dependencies, can be induced efficiently by exploiting certain properties of rule evaluation measures and pruning the label search space accordingly. However, experiments have revealed that multi-label heads are unlikely to be learned by existing methods due to their restrictiveness. To overcome this limitation, we propose a plug-in approach that relaxes the search space pruning used by existing methods in order to introduce a bias towards larger multi-label heads resulting in more expressive rules. We further demonstrate the effectiveness of our approach empirically and show that it does not come with drawbacks in terms of training time or predictive performance.

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“…On the other hand, there are algorithms that adopt the separate-and-conquer strategy used by many traditional rule learners for binary or multi-class classification, e.g. by Ripper [7], and transfer it to MLC [20,22]. Whereas in descriptive rule learning one does usually not aim at discovering rules that minimize a certain (multi-label) loss, the latter approaches employ a heuristic-guided search for rules that optimize a given rule learning heuristic and hence could benefit from the results of this work.…”

Section: Related Workmentioning

confidence: 99%

On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

2019

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Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rule learning algorithms employ a heuristic-guided search for rules that model regularities contained in the training data and it is commonly accepted that the choice of the heuristic has a significant impact on the predictive performance of the learner. Whereas the properties of rule learning heuristics have been studied in the realm of single-label classification, there is no such work taking into account the particularities of multi-label classification. This is surprising, as the quality of multi-label predictions is usually assessed in terms of a variety of different, potentially competing, performance measures that cannot all be optimized by a single learner at the same time. In this work, we show empirically that it is crucial to trade off the consistency and coverage of rules differently, depending on which multi-label measure should be optimized by a model. Based on these findings, we emphasize the need for configurable learners that can flexibly use different heuristics. As our experiments reveal, the choice of the heuristic is not straight-forward, because a search for rules that optimize a measure locally does usually not result in a model that maximizes that measure globally.

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Learning rules for multi-label classification: a stacking and a separate-and-conquer approach

Cited by 31 publications

References 44 publications

Enhancing multilabel classification for food truck recommendation

Enhancing multilabel classification for food truck recommendation

Efficient Discovery of Expressive Multi-label Rules Using Relaxed Pruning

On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

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