A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Charte, David; Charte, Francisco; García, Salvador; Herrera, Francisco

doi:10.1007/s13748-018-00167-7

Cited by 30 publications

(21 citation statements)

References 92 publications

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“…For descriptions of the techniques not covered here, we refer to the works of Athey (2018), Mullainathan and Spiess (2017), Varian (2014) or Wanke and Barros (2016). Singh et al (2016) provide a concise comparison of the advantages and disadvantages of the different techniques, and Charte et al (2019) give an overview on non-standard ML problems.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Bärtl¹,

Krummaker²

2020

Risks

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This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance.

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Section: Supervised Machine Learningmentioning

confidence: 99%

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Bärtl¹,

Krummaker²

2020

Risks

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“…MEKA [37] differs from most other tools in two ways: 5 on the one hand, its objective is to facilitate the execution of Aside from multilabel learning, MEKA also provides tools to accomplish multitarget learning and hierarchical learning. Those are not considered here.…”

Section: Appendix B Mekamentioning

confidence: 99%

“…Single-label learning, also known as standard learning, is probably the most common scenario when working in EDA and DM tasks, but it is certainly not the only one. There are other non-standard modalities [5], such as multilabel learning [6] (MLL), multiinstance learning [7], multiview learning [8], etc. Here we are particularly interested in MLL, since it is the most common case of non-standard learning.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive and Didactic Review on Multilabel Learning Software Tools

Charte

2020

IEEE Access

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Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances. Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category. This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card. Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years. Among them, multilabel learning is arguably the most popular one. These algorithms aim to produce models in which each data pattern may be linked to several categories at once. Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does. However, software tools for multilabel learning tend to be scarce. This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software. It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites. The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.

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“…Preferences are comparative judgments about a set of alternatives or choices. The Label Ranking (LR) problem [ 1 , 2 , 3 ] is a well-known non-standard supervised classification problem [ 4 , 5 ], whose goal is to learn preference models that predict the preferred ranking over a set of class labels for a given unlabeled instance. Practical applications of the LR problem are found in cases where an order of preference (or ranking) for the class labels is required given an input instance.…”

Section: Introductionmentioning

confidence: 99%

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

Rodrigo

Alfaro

Aledo

et al. 2021

Entropy

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The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.

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A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

Cited by 30 publications

References 92 publications

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

A Comprehensive and Didactic Review on Multilabel Learning Software Tools

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

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