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A general framework for boosting feature subset selection algorithms
(Elsevier, 2018)
Feature selection is one of the most important tasks in many machine learning and data mining problems. Due to the increasing size of the problems, removing useless, erroneous or noisy features is frequently an initial ...
Local-based k values for multi-label k-nearest neighbors rule
(Elsevier, 2022)
Multi-label learning is a growing field in machine learning research. Many applications address instances that simultaneously belong to many categories, which cannot be disregarded if optimal results are desired. Among the ...
ReSLAM: Reusable SLAM with heterogeneous cameras
(Elsevier, 2024)
State-of-the-art SLAM methods are designed to work only with the type of camera employed to create the map, and little attention has been paid to the reusability of the maps created. In other words, the maps generated by ...
Semantic segmentation of 3D car parts using UAV-based images
(Elsevier, 2022)
Environment understanding in real-world scenarios has gained an increased interest in research and industry. The advances in data capture and processing allow a high-detailed reconstruction from a set of multi-view images ...
AttenGait: Gait recognition with attention and rich modalities
(Elsevier, 2024)
Current gait recognition systems employ different types of manual attention mechanisms, like horizontal cropping of the input data to guide the training process and extract useful gait signatures for people identification. ...
A new approach for optimal offline time-series segmentation with error bound guarantee
(Elsevier, 2021)
This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) Liu et al. (2008)[, that minimizes the number of segments of the approximation and guarantees the error bound using the L∞-norm. On the ...
Assessing polygonal approximations: A new measurement and a comparative study
(Elsevier, 2023)
Two proposals related to the evaluation of polygonal approximations are presented in this document. First, a new measurement, called normalized compression ratio and adjustment error (NCA), to provide a fair evaluation of ...
PARIS: Partial instance and training set selection. A new scalable approach to multi-label classification
(Elsevier, 2023)
Multi-label classification has recently attracted research interest as a data mining task. Many current applications in data mining address problems that have instances belonging to more than one class. This requires the ...
SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features
(Elsevier, 2021)
Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. ...
CRIBEL: Lifelong learning social network governed by academic institutions: an affordable serverless model in the cloud
(IATED, 2023)
Academic institutions, teachers, students and workers are facing important changes in the way they
organise work and training in new skills demanded by companies in the face of the new challenges of
society in general ...