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Found 9 papers, 6 papers with code
mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms
Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios.
EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search
Machine learning models work better when curated features are provided to them.
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
A Showcase of the Use of Autoencoders in Feature Learning Applications
Autoencoders are techniques for data representation learning based on artificial neural networks.
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction
In this work AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented.
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization
The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification.
Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets
In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed.
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository
New proposals in the field of multi-label learning algorithms have been growing in number steadily over the last few years.
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model.
