61 citations · 68 across the 15 of their papers we have counts for
20 papers
AiGAS-dEVL-RC: An Adaptive Growing Neural Gas Model for Recurrently Drifting Unsupervised Data Streams
Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo +1
Concept drift and extreme verification latency pose significant challenges in data stream learning, particularly when dealing with recurring concept changes in dynamic environments…
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child
Javier Del Ser, Jesus L. Lobo, Heimo Müller +1
World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the…
Can transformative AI shape a new age for our civilization?: Navigating between speculation and reality
Jesus L. Lobo, Javier Del Ser
Artificial Intelligence is widely regarded as a transformative force with the potential to redefine numerous sectors of human civilization. While Artificial Intelligence has evolve…
Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams
Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas +1
Modern digital applications extensively integrate Artificial Intelligence models into their core systems, offering significant advantages for automated decision-making. However, th…
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo +1
The ever-growing speed at which data are generated nowadays, together with the substantial cost of labeling processes cause Machine Learning models to face scenarios in which data…
Managing the unknown: a survey on Open Set Recognition and tangential areas
Marcos Barcina-Blanco, Jesus L. Lobo, Pablo Garcia-Bringas +1
In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage.…
On the Connection between Concept Drift and Uncertainty in Industrial Artificial Intelligence
Jesus L. Lobo, Ibai Laña, Eneko Osaba +1
AI-based digital twins are at the leading edge of the Industry 4.0 revolution, which are technologically empowered by the Internet of Things and real-time data analysis. Informatio…
A Novel Explainable Out-of-Distribution Detection Approach for Spiking Neural Networks
Aitor Martinez Seras, Javier Del Ser, Jesus L. Lobo +2
Research around Spiking Neural Networks has ignited during the last years due to their advantages when compared to traditional neural networks, including their efficient processing…
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Eneko Osaba, Javier Del Ser, Aritz D. Martinez +2
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem ef…
CURIE: A Cellular Automaton for Concept Drift Detection
Jesus L. Lobo, Javier Del Ser, Eneko Osaba +2
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, g…
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo +2
Multitasking optimization is a recently introduced paradigm, focused on the simultaneous solving of multiple optimization problem instances (tasks). The goal of multitasking enviro…
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Javier Del Ser, Ibai Lana, Eric L. Manibardo +5
In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activit…
New Perspectives on the Use of Online Learning for Congestion Level Prediction over Traffic Data
Eric L. Manibardo, Ibai Laña, Jesus L. Lobo +1
This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predict…
Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithmic Design, Performance Comparison and Genetic Transferability Analysis
Eneko Osaba, Aritz D. Martinez, Jesus L. Lobo +2
Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a sing…
LUNAR: Cellular Automata for Drifting Data Streams
Jesus L. Lobo, Javier Del Ser, Francisco Herrera
With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world a…
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks
Piotr S. Maciąg, Marzena Kryszkiewicz, Robert Bembenik +2
Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labe…
Rank aggregation for non-stationary data streams
Ekhine Irurozki, Jesus Lobo, Aritz Perez +1
We consider the problem of learning over non-stationary ranking streams. The rankings can be interpreted as the preferences of a population and the non-stationarity means that the…
Spiking Neural Networks and Online Learning: An Overview and Perspectives
Jesus L. Lobo, Javier Del Ser, Albert Bifet +1
Applications that generate huge amounts of data in the form of fast streams are becoming increasingly prevalent, being therefore necessary to learn in an online manner. These condi…
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Jesus L. Lobo, Izaskun Oregi, Albert Bifet +1
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learning…
Real-time Electrical Power Prediction in a Combined Cycle Power Plant
Jesus L. Lobo, Igor Ballesteros, Izaskun Oregi +1
The prediction of electrical power in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power output can vary depending on envir…