A5078909305- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Data Mining Algorithms and Applications
- Evolutionary Algorithms and Applications
- Metaheuristic Optimization Algorithms Research
- Face and Expression Recognition
- Data Stream Mining Techniques
- Electricity Theft Detection Techniques
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
- Advanced Multi-Objective Optimization Algorithms
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Advanced Clustering Algorithms Research
- Data Management and Algorithms
- Biometric Identification and Security
- Machine Learning and Algorithms
- Rough Sets and Fuzzy Logic
- Artificial Intelligence in Healthcare
- Advanced Statistical Methods and Models
- Data Analysis with R
- Big Data and Business Intelligence
- Bayesian Methods and Mixture Models
- Bayesian Modeling and Causal Inference
- Financial Distress and Bankruptcy Prediction
Universidad de Granada
2016-2025
Instituto Andaluz de Ciencias de la Tierra
2018-2025
IEEE Computer Society
2021
Universidad de Jaén
2008-2020
Instituto Venezolano de Investigaciones Científicas
2020
John Wiley & Sons (United States)
2017-2019
Tecnológico de Monterrey
2018
King Abdulaziz University
2015-2017
Intelligent Systems Research (United States)
2017
Universidad de Oviedo
2010
The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This due to its simplicity design procedure, as well robustness when applied different type problems. Since publication 2002, SMOTE has proven successful a variety applications several domains. also inspired approaches counter issue class imbalance, and significantly contributed new supervised paradigms, including multilabel...
The nearest neighbor classifier is one of the most used and well-known techniques for performing recognition tasks. It has also demonstrated itself to be useful algorithms in data mining spite its simplicity. However, suffers from several drawbacks such as high storage requirements, low efficiency classification response, noise tolerance. These weaknesses have been subject study many researchers solutions proposed. Among them, promising consists reducing establishing a rule (training data)...
The massive growth in the scale of data has been observed recent years being a key factor Big Data scenario. can be defined as high volume, velocity and variety that require new high-performance processing. Addressing big is challenging time-demanding task requires large computational infrastructure to ensure successful processing analysis. presence preprocessing methods for mining reviewed this paper. definition, characteristics, categorization approaches are introduced. connection between...
Discretization is an essential preprocessing technique used in many knowledge discovery and data mining tasks. Its main goal to transform a set of continuous attributes into discrete ones, by associating categorical values intervals thus transforming quantitative qualitative data. In this manner, symbolic algorithms can be applied over the representation information simplified, making it more concise specific. The literature provides numerous proposals discretization some attempts categorize...
Learning with imbalanced data is one of the recent challenges in machine learning. Various solutions have been proposed order to find a treatment for this problem, such as modifying methods or application preprocessing stage. Within focused on balancing data, two tendencies exist: reduce set examples (undersampling) replicate minority class (oversampling). Undersampling datasets could be considered prototype selection procedure purpose achieve high classification rate, avoiding bias toward...
The nearest neighbor (NN) rule is one of the most successfully used techniques to resolve classification and pattern recognition tasks. Despite its high accuracy, this suffers from several shortcomings in time response, noise sensitivity, storage requirements. These weaknesses have been tackled by many different approaches, including a good well-known solution that we can find literature, which consists reduction data for (training data). Prototype be divided into two are known as prototype...
This paper introduces the 3 rd major release of KEEL Software.KEEL is an open source Java framework (GPLv3 license) that provides a number modules to perform wide variety data mining tasks.It includes tools management, design multiple kind experiments, statistical analyses, etc.This also contains KEEL-dataset, repository for learning tasks featuring partitions and algorithms' results over these problems.In this work, we describe most recent components added 3.0, including new semi-supervised...