58 search results - page 3 / 12 » Eliminating redundancy and irrelevance using a new MLP-based... |
MCS
2007
Springer
14 years 1 months ago
2007
Springer
Selecting the optimal number of features in a classifier ensemble normally requires a validation set or cross-validation techniques. In this paper, feature ranking is combined with...
ICANN
2007
Springer
13 years 11 months ago
2007
Springer
Abstract. In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information ...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
Feature selection, as a preprocessing step to machine learning, has been effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improvin...
BMCBI
2007
13 years 7 months ago
2007
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
ISDA
2009
IEEE
14 years 2 months ago
2009
IEEE
For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors t...