Abstract. This paper addresses the problem of feature selection within classification processes. We present a comparison of a feature subset selection with respect to two boosting ...
Given sensors to detect object use, commonsense priors of object usage in activities can reduce the need for labeled data in learning activity models. It is often useful, however,...
Shiaokai Wang, William Pentney, Ana-Maria Popescu,...
Feature interaction presents a challenge to feature selection for classification. A feature by itself may have little correlation with the target concept, but when it is combined...
Abstract. Feature selection has improved the performance of text clustering. Global feature selection tries to identify a single subset of features which are relevant to all cluste...
Marcelo N. Ribeiro, Manoel J. R. Neto, Ricardo Bas...
— The application of feature selection techniques greatly reduces the computational cost of classifying highdimensional data. Feature selection algorithms of varying performance ...
Lauren Burrell, Otis Smart, George J. Georgoulas, ...
Sequential forward selection (SFS) is one of the most widely used feature selection procedures. It starts with an empty set and adds one feature at each step. The estimate of the ...
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
In recent years, unsupervised gene (feature) selection has become an integral part of microarray analysis because of the large number of genes and complexity in biological systems....
Abstract. Classifier decision fusion has been shown to act in a manner analogous to the back-projection of Radon transformations when individual classifier feature sets are non o...
In this paper we investigate the classification of mental tasks based on electroencephalographic (EEG) data for Brain Computer Interfaces (BCI) in two scenarios: off line and on-l...