Buscar
Mostrando ítems 1-7 de 7
Artículo
Filter‑based feature selection in the context of evolutionary neural networks in supervised machine learning
(Springer, 2020)
This paper presents a workbench to get simple neural classifcation models based on product evolutionary networks via a prior data preparation at attribute level by means of flter-based feature selection. Therefore, the ...
Ponencia
Best Agglomerative Ranked Subset for Feature Selection
(2008-09)
The enormous increase of the size in databases makes finding an optimal subset of features extremely difficult. In this paper, a new feature selection method is proposed that will allow any subset evaluator -including the ...
Artículo
Semi-wrapper feature subset selector for feed-forward neural networks: Applications to binary and multi-class classification problems
(ScienceDirect, 2019-08-11)
This paper explores widely the data preparation stage within the process of knowledge discovery and data mining via feature subset selection in the context of two very well-known neural models: radial basis function neural ...
Capítulo de Libro
Efficient Incremental-Ranked Feature Selection in Massive Data
(Chapman and Hall/CRC, 2007)
Artículo
Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks
(Taylor and Francis, 2016)
This paper presents a quality enhancement of the selected features by a hybrid filter-based jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based measure via merging new features which ...
Artículo
Finding Defective Software Modules by Means of Data Mining Techniques
(IEEE, 2009-07)
The characterization of defective modules in software engineering remains a challenge. In this work, we use data mining techniques to search for rules that indicate modules with a high probability of being defective. ...
Ponencia
SNN: A Supervised Clustering Algorithm
(Springer, 2001-06)
In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying interesting distributions in the underlying data in the labelled databases. We introduces the theory ...