Search Results for author: Francisco Herrera

Found 48 papers, 19 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 Prediction

Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization

1 code implementation20 May 2022 Javier Del Ser, Alejandro Barredo-Arrieta, Natalia Díaz-Rodríguez, Francisco Herrera, Andreas Holzinger

To this end, we present a novel framework for the generation of counterfactual examples which formulates its goal as a multi-objective optimization problem balancing three different objectives: 1) plausibility, i. e., the likeliness of the counterfactual of being possible as per the distribution of the input data; 2) intensity of the changes to the original input; and 3) adversarial power, namely, the variability of the model's output induced by the counterfactual.

Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization

no code implementations21 Apr 2022 Alejandro Rosales-Pérez, Salvador García, Francisco Herrera

The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters.

Bilevel Optimization Classification +1

EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks

no code implementations8 Feb 2022 Javier Poyatos, Daniel Molina, Aritz. D. Martinez, Javier Del Ser, Francisco Herrera

Depending on its solution encoding strategy, our proposed model can either perform optimized pruning or feature selection over the densely connected part of the neural network.

feature selection Transfer Learning

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.

CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study

no code implementations25 May 2021 Francisco Pérez-Hernández, José Rodríguez-Ortega, Yassir Benhammou, Francisco Herrera, Siham Tabik

However, the detection of such infrastructures is complex as they have highly variable shapes and sizes, i. e., some infrastructures, such as electrical substations, are too small while others, such as airports, are too large.

Anomaly Detection Change Detection

MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance

no code implementations23 Apr 2021 Roberto Olmos, Siham Tabik, Francisco Perez-Hernandez, Alberto Lamas, Francisco Herrera

Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable.

CURIE: A Cellular Automaton for Concept Drift Detection

1 code implementation21 Sep 2020 Jesus L. Lobo, Javier Del Ser, Eneko Osaba, Albert Bifet, Francisco Herrera

Specifically, in CU RIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream.

Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges

no code implementations9 Aug 2020 Aritz D. Martinez, Javier Del Ser, Esther Villar-Rodriguez, Eneko Osaba, Javier Poyatos, Siham Tabik, Daniel Molina, Francisco Herrera

In summary, three axes - optimization and taxonomy, critical analysis, and challenges - which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.

Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning

1 code implementation29 Jul 2020 Nuria Rodríguez-Barroso, Eugenio Martínez-Cámara, M. Victoria Luzón, Francisco Herrera

We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model.

Data Poisoning Federated Learning +1

Federated Learning and Differential Privacy: Software tools analysis, the FL framework and methodological guidelines for preserving data privacy

no code implementations2 Jul 2020 Nuria Rodríguez-Barroso, Goran Stipcich, Daniel Jiménez-López, José Antonio Ruiz-Millán, Eugenio Martínez-Cámara, Gerardo González-Seco, M. Victoria Luzón, Miguel Ángel Veganzones, Francisco Herrera

The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use.

BIG-bench Machine Learning Federated Learning

An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges

1 code implementation21 May 2020 David Charte, Francisco Charte, María J. del Jesus, Francisco Herrera

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.

Image Denoising Representation Learning

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis

no code implementations24 Mar 2020 Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo, Javier Del Ser, Francisco Herrera

Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration.

Transfer Learning Traveling Salesman Problem

Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise

1 code implementation5 Mar 2020 Sergio González, Salvador García, Sheng-Tun Li, Robert John, Francisco Herrera

This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).

Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization

no code implementations25 Feb 2020 Aritz D. Martinez, Eneko Osaba, Javier Del Ser, Francisco Herrera

A thorough experimentation is presented and discussed so as to assess the performance of the framework, its comparison to the traditional methodology for Transfer Learning in terms of convergence, speed and policy quality , and the intertask relationships found and exploited over the search process.

Q-Learning reinforcement-learning +1

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

no code implementations19 Feb 2020 Daniel Molina, Javier Poyatos, Javier Del Ser, Salvador García, Amir Hussain, Francisco Herrera

Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper.

LUNAR: Cellular Automata for Drifting Data Streams

no code implementations6 Feb 2020 Jesus L. Lobo, Javier Del Ser, Francisco Herrera

A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms).

online learning

Deep Learning in Video Multi-Object Tracking: A Survey

no code implementations18 Jul 2019 Gioele Ciaparrone, Francisco Luque Sánchez, Siham Tabik, Luigi Troiano, Roberto Tagliaferri, Francisco Herrera

The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video.

Multi-Object Tracking Multiple Object Tracking

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods

no code implementations29 Nov 2018 David Charte, Francisco Charte, Salvador García, Francisco Herrera

This field is subdivided into multiple areas, among which the best known are supervised learning (e. g. classification and regression) and unsupervised learning (e. g. clustering and association rules).

Classification General Classification

OCAPIS: R package for Ordinal Classification And Preprocessing In Scala

no code implementations23 Oct 2018 M. Cristina Heredia-Gómez, Salvador García, Pedro Antonio Gutiérrez, Francisco Herrera

The classification and pre-processing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems.

Classification General Classification

BELIEF: A distance-based redundancy-proof feature selection method for Big Data

1 code implementation16 Apr 2018 Sergio Ramírez-Gallego, Salvador García, Ning Xiong, Francisco Herrera

Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).

feature selection

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

Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets

no code implementations14 Feb 2018 Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera

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.

Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization

no code implementations14 Feb 2018 Francisco Charte, Antonio J. Rivera, María J. del Jesus, Francisco Herrera

The learning from imbalanced data is a deeply studied problem in standard classification and, in recent times, also in multilabel classification.

General Classification

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

1 code implementation4 Jan 2018 David Charte, Francisco Charte, Salvador García, María J. del Jesus, Francisco Herrera

Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model.

Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery

no code implementations3 Jun 2017 Emilio Guirado, Siham Tabik, Domingo Alcaraz-Segura, Javier Cabello, Francisco Herrera

There is a growing demand for accurate high-resolution land cover maps in many fields, e. g., in land-use planning and biodiversity conservation.

Data Augmentation Object Recognition +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

On the use of convolutional neural networks for robust classification of multiple fingerprint captures

no code implementations21 Mar 2017 Daniel Peralta, Isaac Triguero, Salvador García, Yvan Saeys, Jose M. Benitez, Francisco Herrera

In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction.

Classification General Classification +1

Automatic Handgun Detection Alarm in Videos Using Deep Learning

1 code implementation16 Feb 2017 Roberto Olmos, Siham Tabik, Francisco Herrera

Current surveillance and control systems still require human supervision and intervention.

Region Proposal

Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification

no code implementations12 Feb 2017 Andrés Herrera-Poyatos, Francisco Herrera

The lack of diversity in a genetic algorithm's population may lead to a bad performance of the genetic operators since there is not an equilibrium between exploration and exploitation.

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