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Mostrando ítems 1-10 de 16
Artículo
Selecting the best measures to discover quantitative association rules
(Elsevier, 2014)
The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly ...
Artículo
Obtaining optimal quality measures for quantitative association rules
(Elsevier, 2016)
There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures ...
Artículo
A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation
(Elsevier, 2017)
Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes involved in breast cancer can be identified by immunohistochemistry. However, this method has ...
Artículo
MRQAR: A generic MapReduce framework to discover quantitative association rules in big data problems
(Elsevier, 2018)
Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques ...
Artículo
Evolutionary association rules for total ozone content modeling from satellite observations
(Elsevier, 2011)
In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quan titative Association Rules) that extends a recently published algorithm by the authors and we describe its ap plication ...
Artículo
Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance
(Elsevier, 2020)
Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often ...
Artículo
Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets
(iOS Press, 2015)
Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes in a subspace of instances from datasets. Genetic algorithms have been extensively used to find ...
Artículo
Applications of Computational Intelligence in Time Series
(Hindawi, 2017)
Artículo
Machine learning techniques to discover genes with potential prognosis role in Alzheimer’s disease using different biological sources
(Elsevier, 2017)
Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence ex pected to rise over the next decades. Unconventional strategies for elucidating the genetic mechanisms are necessary ...
Artículo
Discovering gene association networks by multi-objective evolutionary quantitative association rules
(Elsevier, 2014)
In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks ...