A5014834933- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Neural Networks and Applications
- Advanced Multi-Objective Optimization Algorithms
- Face and Expression Recognition
- Imbalanced Data Classification Techniques
- Machine Learning and Data Classification
- Text and Document Classification Technologies
- Human Mobility and Location-Based Analysis
- RNA and protein synthesis mechanisms
- Fault Detection and Control Systems
- Machine Learning in Bioinformatics
- Data-Driven Disease Surveillance
- Numerical Methods and Algorithms
- Statistical and Computational Modeling
- Video Surveillance and Tracking Methods
- Geographic Information Systems Studies
- Online Learning and Analytics
- Control Systems and Identification
- Fuzzy Logic and Control Systems
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Algorithms and Data Compression
- Vehicle License Plate Recognition
- E-Learning and Knowledge Management
University of Córdoba
2008-2025
Cordoba University
2005
International Center for Numerical Methods in Engineering
2005
This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is recent paradigm in evolutionary computation that allows the effective modeling of environments. Although theoretically, single with sufficient number neurons hidden layer would suffice to solve any problem, practice many real-world problems are too hard construct appropriate them. In such problems, ensembles successful alternative. Nevertheless, design complex task. this...
In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The theoretical distribution genes best individuals in population. proposed takes into account localization and dispersion features objective these would be inherited by offspring. Our aim optimization balance between exploration exploitation search process. order to test efficiency robustness crossover, have used set functions...
We present a new method of multiclass classification based on the combination one-vs-all and modification one-vs-one method. This methods proposed enforces strength both methods. A study behavior two identifies some sources their failure. The performance classifier can be improved if are combined in one, such way that main failure partially avoided.
Multi-label learning is a growing field in machine research. Many applications address instances that simultaneously belong to many categories, which cannot be disregarded if optimal results are desired. Among the algorithms developed for multi-label learning, k-nearest neighbor method among most successful. However, difficult classification task, such as challenge arises approach assignment of appropriate value k. Although suitable might obtained using cross-validation, it unlikely same...
This study explores land use classification in Trento using supervised learning techniques combined with call detail records (CDRs) as a proxy for human activity. Located an alpine environment, presents unique geographic challenges, including varied terrain and sparse network coverage, making it ideal case testing the robustness of approaches. By analyzing spatiotemporal patterns CDRs, we trained evaluated several algorithms, k-nearest neighbors (kNN), support vector machines (SVM), random...
Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited individuals. In this study, we introduce SAMPLID, a new Supervised Approach Meaningful Place Identification, based on providing knowledge base focused specific problem aim to solve (e.g., home/work identification). This approach allows tackle place identification from supervised perspective, offering an alternative unsupervised clustering techniques. These techniques rely data...