Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

Pascual-Fontanilles, Jordi; Valls, Aïda; Moreno, Antonio; Romero-Aroca, Pedro

doi:10.3233/faia210136

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…As future work, we should test the proposed method with other datasets to confirm the observations. Then, we plan to test the method with other aggregation operators, as well as to study how it could make use of the dynamic updating method proposed in [4].…”

Section: Discussionmentioning

confidence: 99%

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Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Pascual-Fontanilles

Lhotska

Moreno

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Fuzzy Random Forests are well-known Machine Learning ensemble methods. They combine the outputs of multiple Fuzzy Decision Trees to improve the classification performance. Moreover, they can deal with data uncertainty and imprecision thanks to the use of fuzzy logic. Although many classification tasks are binary, in some situations we face the problem of classifying data into a set of ordered categories. This is a particular case of multi-class classification where the order between the classes is relevant, for example in medical diagnosis to detect the severity of a disease. In this paper, we explain how a binary Fuzzy Random Forest may be adapted to deal with ordinal classification. The work is focused on the prediction stage, not on the construction of the fuzzy trees. When a new instance arrives, the rules activation is done with the usual fuzzy operators, but the aggregation of the outputs given by the different rules and trees has been redefined. In particular, we present a procedure for managing the conflicting cases where different classes are predicted with similar support. The support of the classes is calculated using the OWA operator that permits to model the concept of majority agreement.

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Section: Discussionmentioning

confidence: 99%

“…In the previous binary approach, we tested several metrics for weighting the trees. An average accuracy balancing sensitivity (2/3) and specificity (1/3) was used [4]. This balanced accuracy is specially useful in domains such as the medical one, in which a good sensitivity is a priority in order to avoid false negatives.…”

Section: Weighted Votingmentioning

confidence: 99%

Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Pascual-Fontanilles

Lhotska

Moreno

et al. 2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

Continuous Dynamic Update of Fuzzy Random Forests

Pascual-Fontanilles

Valls

Moreno

et al. 2022

Int J Comput Intell Syst

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Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.

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Iterative Update of a Random Forest Classifier for Diabetic Retinopathy

Cited by 2 publications

References 16 publications

Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Adapting a Fuzzy Random Forest for Ordinal Multi-Class Classification

Continuous Dynamic Update of Fuzzy Random Forests

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