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Found 59 papers, 23 papers with code
An Information Theoretic Feature Selection Framework for Big Data under Apache Spark
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.
Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification
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.
Automatic Handgun Detection Alarm in Videos Using Deep Learning
Current surveillance and control systems still require human supervision and intervention.
On the use of convolutional neural networks for robust classification of multiple fingerprint captures
In our experiments, convolutional neural networks yielded better accuracy and penetration rate than state-of-the-art classifiers based on explicit feature extraction.
Enabling Smart Data: Noise filtering in Big Data classification
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used.
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery
There is a growing demand for accurate high-resolution land cover maps in many fields, e. g., in land-use planning and biodiversity conservation.
An Information Theory-Based Feature Selection Framework for Big Data Under Apache Spark
With the advent of extremely high dimensional datasets, dimensionality reduction techniques are becoming mandatory.
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.
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.
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.
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.
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation
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.
BELIEF: A distance-based redundancy-proof feature selection method for Big Data
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).
DPASF: A Flink Library for Streaming Data preprocessing
Data preprocessing techniques are devoted to correct or alleviate errors in data.
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala
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.
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods
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).
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)
This tutorial provides a theoretical background and foundations on this topic and a comprehensive experimental analysis of the most-known algorithms.
Deep Learning in Video Multi-Object Tracking: A Survey
The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video.
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Smart Data driven Decision Trees Ensemble Methodology for Imbalanced Big Data
Ensembles have shown to be able to successfully address imbalanced data problems.
LUNAR: Cellular Automata for Drifting Data Streams
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).
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
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.
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
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.
Fuzzy k-Nearest Neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise
This paper proposes a new model based on Fuzzy k-Nearest Neighbors for classification with monotonic constraints, Monotonic Fuzzy k-NN (MonFkNN).
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
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.
Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics
In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task.
A Showcase of the Use of Autoencoders in Feature Learning Applications
Autoencoders are techniques for data representation learning based on artificial neural networks.
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.
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
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.
Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect
However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance.
Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning
We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model.
Sentiment Analysis based Multi-person Multi-criteria Decision Making Methodology using Natural Language Processing and Deep Learning for Smarter Decision Aid. Case study of restaurant choice using TripAdvisor reviews
We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making.
Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges
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.
CURIE: A Cellular Automaton for Concept Drift Detection
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.
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously.
MULTICAST: MULTI Confirmation-level Alarm SysTem based on CNN and LSTM to mitigate false alarms for handgun detection in video-surveillance
Despite the constant advances in computer vision, integrating modern single-image detectors in real-time handgun alarm systems in video-surveillance is still debatable.
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
We tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph.
CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study
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 in Predictive Maintenance: A New Evaluation Framework for Temporal Unsupervised Anomaly Detection Algorithms
The research in anomaly detection lacks a unified definition of what represents an anomalous instance.
A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training
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.
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions
They can be applied as a preprocessing stage for a binary classification problem.
Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness.
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence.
EvoPruneDeepTL: An Evolutionary Pruning Model for Transfer Learning based Deep Neural Networks
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.
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization
The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters.
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
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.
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)
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.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of study
There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations.
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness
MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models.
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective.
General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance
Most applications of Artificial Intelligence (AI) are designed for a confined and specific task.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust
Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it.
Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges
This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions.
SHIELD: A regularization technique for eXplainable Artificial Intelligence
As Artificial Intelligence systems become integral across domains, the demand for explainability grows.
An Interpretable Client Decision Tree Aggregation process for Federated Learning
In this paper, we propose an Interpretable Client Decision Tree Aggregation process for Federated Learning scenarios that keeps the interpretability and the precision of the base decision trees used for the aggregation.
FLEX: FLEXible Federated Learning Framework
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount.
