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Capítulo de Libro
Mining Low Dimensionality Data Streams of Continuous Attributes
(2003)
This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, high–cardinality, time–changing data streams. Within the Supervised Learning field, our approach, named ...
Artículo
Método de inducción de reglas de clasificación oblicuas mediante un algoritmo evolutivo
(Universidad Autónoma de Bucaramanga, 2002-06-01)
En este artículo presentamos un nuevo método, de nominado OBLIC, para inducción de reglas de clasificación oblicuas no jerárquicas a partir de un conjunto de datos etiquetados. La base del método es un algoritmo evolutivo ...
Artículo
Asynchronous dual-pipeline deep learning framework for online data stream classification
(IOS Press, 2020)
Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming ...
Ponencia
Low Dimensionality or Same Subsets as a Result of Feature Selection: An In-Depth Roadma
(Springer, 2017-06)
This paper addresses the situation that may happen after the application of feature subset selection in terms of a reduced number of selected features or even same solutions obtained by different algorithms. The data mining ...
Ponencia
SmartFD: A Real Big Data Application for Electrical Fraud Detection
(Springer, 2018)
The main objective of this paper is the application of big data analytics to a real case in the field of smart electric networks. Smart meters are not only elements to measure consumption, but they also con stitute a ...
Ponencia
Finding Defective Modules from Highly Unbalanced Datasets
(Sociedad de Ingeniería de Software y Tecnologías de Desarrollo de Software (SISTEDES), 2008-10)
Many software engineering datasets are highly unbalanced, i.e., the number of instances of a one class outnumber the number of instances of the other class. In this work, we analyse two balancing techniques with two common ...
Ponencia
On the performance of deep learning models for time series classification in streaming
(Springer, 2020)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...
Artículo
Data streams classification using deep learning under different speeds and drifts
(Oxford University Press, 2022)
Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, ...