@article {Charte:ClassInformedAEs,
	title = {Reducing Data Complexity using Autoencoders with Class-informed Loss Functions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {In Press},
	year = {2021},
	note = {PID2019-107793GB-I00},
	publisher = {IEEE},
	abstract = {Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher{\textquoteright}s discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task.},
	issn = {0162-8828},
	doi = {10.1109/TPAMI.2021.3127698},
	author = {David Charte and Francisco Charte and Herrera, Francisco}
}
@conference {Charte:HAIS21,
	title = {Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study},
	booktitle = {Hybrid Artificial Intelligent Systems},
	year = {2021},
	note = {PID2020-119478GB-I00},
	pages = {305{\textendash}315},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Datasets from real-world applications usually deal with many variables and present difficulties when modeling them with traditional classifiers. There is a variety of feature selection and extraction tools that may help with the dimensionality problem, but most of them do not focus on the complexity of the classes. In this paper, a new autoencoder-based model for addressing class complexity in data is introduced, aiming to extract features that present classes in a more separable fashion, thus simplifying the classification task. This is possible thanks to a combination of the standard reconstruction error with a least-squares support vector machine loss function. This model is then applied to a practical use case: classification of chest X-rays according to the presence of COVID-19, showing that learning features that increase linear class separability can boost classification performance. For this purpose, a specific convolutional autoencoder architecture has been designed and trained using the recently published COVIDGR dataset. The proposed model is evaluated by means of several traditional classifiers and metrics, in order to establish the improvements caused by the extracted features. The advantages of using a feature learner and traditional classifiers are also discussed.},
	isbn = {978-3-030-86271-8},
	author = {David Charte and Sevillano-Garc{\'\i}a, Iv{\'a}n and Lucena-Gonz{\'a}lez, Mar{\'\i}a Jes{\'u}s and Mart{\'\i}n-Rodr{\'\i}guez, Jos{\'e} Luis and Francisco Charte and Herrera, Francisco},
	editor = {Sanjurjo Gonz{\'a}lez, Hugo and Pastor L{\'o}pez, Iker and Garc{\'\i}a Bringas, Pablo and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
