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Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification

arXiv 2302.10430 ↗First posted 21 Feb 2023cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Prediction of multi-dimensional labels plays an important role in machine learning problems. We found that the classical binary labels could not reflect the contents and their relationships in an instance. Hence, we propose a multi-label classification model based on interval type-2 fuzzy logic. In the proposed model, we use a deep neural network to predict the type-1 fuzzy membership of an instance and another one to predict the fuzzifiers of the membership to generate interval type-2 fuzzy memberships. We also propose a loss function to measure the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model. The experiments validate that our approach outperforms baselines on multi-label classification benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 21 Feb 2023.

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References · 20

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8 more
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