Search Results for author: Julián Luengo

Found 7 papers, 2 papers with code

TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)

1 code implementation7 Jun 2022 Ignacio Aguilera-Martos, Ángel M. García-Vico, Julián Luengo, Sergio Damas, Francisco J. Melero, José Javier Valle-Alonso, Francisco Herrera

The combination of convolutional and recurrent neural networks is a promising framework that allows the extraction of high-quality spatio-temporal features together with its temporal dependencies, which is key for time series prediction problems such as forecasting, classification or anomaly detection, amongst others.

Anomaly Detection Time Series +1

A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training

1 code implementation8 Sep 2021 Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera

This algorithm filters and relabels instances of the training set based on the predictions and their probabilities made by the backbone neural network during the training process.

Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation

no code implementations27 Mar 2018 Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups.

Data Augmentation General Classification +1

Enabling Smart Data: Noise filtering in Big Data classification

no code implementations6 Apr 2017 Diego García-Gil, Julián Luengo, Salvador García, Francisco Herrera

In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used.

Classification General Classification

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