All Outputs (55)

Local-global methods for generalised solar irradiance forecasting (2024)
Journal Article

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-serie... Read More about Local-global methods for generalised solar irradiance forecasting.

General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance (2023)
Journal Article

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them.... Read More about General Purpose Artificial Intelligence Systems (GPAIS): Properties, definition, taxonomy, societal implications and responsible governance.

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data (2023)
Conference Proceeding

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models t... Read More about Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data.

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

Identifying bird species by their calls in Soundscapes (2023)
Journal Article

In many real data science problems, it is common to encounter a domain mismatch between the training and testing datasets, which means that solutions designed for one may not transfer well to the other due to their differences. An example of such was... Read More about Identifying bird species by their calls in Soundscapes.

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort (2022)
Journal Article

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in Forced Vital Capacity (FVC) is the main indicator of progression, however missingness prevents long-term analysis o... Read More about Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort.

Feature Importance Identification for Time Series Classifiers (2022)
Conference Proceeding

Time series classification is a challenging research area where machine learning techniques such as deep learning perform well, yet lack interpretability. Identifying the most important features for such classifiers provides a pathway to improving th... Read More about Feature Importance Identification for Time Series Classifiers.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.

SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction (2021)
Journal Article

Real-world optimisation problems pose domain specific challenges that often require an ad-hoc algorithmic design to be efficiently addressed. The present paper investigates the optimisation of a key stage in data mining, known as instance reduction,... Read More about SPMS-ALS: A Single-Point Memetic structure with accelerated local search for instance reduction.

Beyond global and local multi-target learning (2021)
Journal Article

In multi-target prediction, an instance has to be classified along multiple target variables at the same time, where each target represents a category or numerical value. There are several strategies to tackle multi-target prediction problems: the lo... Read More about Beyond global and local multi-target learning.

L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout (2021)
Journal Article

Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between examples in a learned metric space based on convolutional neural network... Read More about L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Decomposition-Fusion for Label Distribution Learning (2020)
Journal Article

Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life... Read More about Decomposition-Fusion for Label Distribution Learning.

Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data (2020)
Journal Article

It is recognized the importance of knowing the descriptive properties of a dataset when tackling a data science problem. Having information about the redundancy, complexity and density of a problem allows us to make decisions as to which data preproc... Read More about Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data.

A Local Search with a Surrogate Assisted Option for Instance Reduction (2020)
Conference Proceeding

© 2020, Springer Nature Switzerland AG. In data mining, instance reduction is a key data pre-processing step that simplifies and cleans raw data, by either selecting or creating new samples, before applying a learning algorithm. This usually yields t... Read More about A Local Search with a Surrogate Assisted Option for Instance Reduction.

Galaxy Image Classification Based on Citizen Science Data: A Comparative Study (2020)
Journal Article

Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers h... Read More about Galaxy Image Classification Based on Citizen Science Data: A Comparative Study.

Multigranulation Super-Trust Model for Attribute Reduction (2020)
Journal Article

As big data often contains a significant amount of uncertain, unstructured, and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information... Read More about Multigranulation Super-Trust Model for Attribute Reduction.

Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems (2020)
Book Chapter

© Springer Nature Switzerland AG 2020. Traffic forecasting is a well-known strategy that supports road users and decision-makers to plan their movements on the roads and to improve the management of traffic, respectively. Current data availability an... Read More about Evaluating Automated Machine Learning on Supervised Regression Traffic Forecasting Problems.

Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data (2019)
Journal Article

One of the best-known and most effective methods in supervised classification is the k nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, hig... Read More about Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data.

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study (2019)
Journal Article

Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of... Read More about Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study.