MLDA: A tool for analyzing multi-label datasets

Moyano, Jose M.; Gibaja, Eva; Ventura, Sebastián

doi:10.1016/j.knosys.2017.01.018

Cited by 23 publications

(9 citation statements)

References 4 publications

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“…In order to characterize the MLC datasets, we calculate various meta-features. All meta-features are calculated using two Java-based libraries, i.e., MLDA 25 , and MULAN 19 , that implement the meta-features. In Fig.…”

Section: Methodsmentioning

confidence: 99%

A catalogue with semantic annotations makes multilabel datasets FAIR

Kostovska

Bogatinovski

Džeroski

et al. 2022

Sci Rep

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Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robust, and trustworthy benchmarking is of utmost importance for the further development of the field. We believe that this can be achieved by adhering to the recently emerged data management standards, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, and Technology) principles. We introduce an ontology-based online catalogue of MLC datasets originating from various application domains following these principles. The catalogue extensively describes many MLC datasets with comprehensible meta-features, MLC-specific semantic descriptions, and different data provenance information. The MLC data catalogue is available at: http://semantichub.ijs.si/MLCdatasets.

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Section: Methodsmentioning

confidence: 99%

A catalogue with semantic annotations makes multilabel datasets FAIR

Kostovska

Bogatinovski

Džeroski

et al. 2022

Sci Rep

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“…As we have discussed above, chaining approaches can only be expected to gain an advantage over, e.g., BR if there are global or local dependencies between the labels in the dataset, which can be picked up and modeled by the learner. For instance, there is evidence that yeast and enron contain mostly global dependencies whereas scene also exhibits local dependencies (Papagiannopoulou et al, 2015;Loza Mencía and Janssen, 2016;Moyano et al, 2017). Unfortunately, so far only few works have tried to systematically analyze these characteristics.…”

Section: Datasetsmentioning

confidence: 99%

Tree-Based Dynamic Classifier Chains

Mencía¹,

Kulessa²,

Bohlender³

et al. 2021

Preprint

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Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naïve implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.

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“…Despite the important practical relevance of the MLC task, the available work on explaining the predictive performance of MLC methods with dataset properties, particularly the aspect of investigating the comprehensible meta knowledge for MLC, is very scarce. To begin with, an overview of available meta features for MLC is given in [42]. The meta features are divided into six groups without providing semantic links among the groups.…”

Section: Related Workmentioning

confidence: 99%

“…There are many MLC-specific meta features that can characterize different aspects of the MLC data sets [48,42]. We analyze each of the available meta features and design a novel taxonomy to better organize and catalogue them (as illustrated in Figure 1).…”

Section: Meta Features For Mlcmentioning

confidence: 99%

Explaining the Performance of Multi-label Classification Methods with Data Set Properties

Bogatinovski¹,

Todorovski²,

Džeroski³

et al. 2021

Preprint

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Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning algorithms. In this paper, we present a comprehensive meta-learning study of data sets and methods for multi-label classification (MLC). MLC is a practically relevant machine learning task where each example is labelled with multiple labels simultaneously. Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data. The main findings of this study are as follows. First, the most prominent meta features that describe the space of MLC data sets are the ones assessing different aspects of the label space. Second, the meta models show that the most important meta features describe the label space, and, the meta features describing the relationships among the labels tend to occur a bit more often than the meta features describing the distributions between and within the individual labels. Third, the optimization of the hyperparameters can improve the predictive performance, however, quite often the extent of the improvements does not always justify the resource utilization.

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MLDA: A tool for analyzing multi-label datasets

Cited by 23 publications

References 4 publications

A catalogue with semantic annotations makes multilabel datasets FAIR

A catalogue with semantic annotations makes multilabel datasets FAIR

Tree-Based Dynamic Classifier Chains

Explaining the Performance of Multi-label Classification Methods with Data Set Properties

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