Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular attribute selection technique for classification that yields good results. However, it can run the risk of overfitting because of the extent of the search and the extensive use of internal cross-validation. Moreover, although wrapper evaluators tend to achieve superior accuracy compared to filters, they face a high computational cost. The problems of overfitting and high runtime occur in particular on high-dimensional datasets, like microarray data. We investigate Linear Forward Selection, a technique to reduce the number of attributes expansions in each forward selection step. Our experiments demonstrate that this approach is faster, finds ...
The identification of atypical observations and the immunization of data analysis against both outli...
When dealing with high dimensional and low sample size data, feature selection is often needed to he...
Microarray data usually contain a large number of genes, but a small number of samples. Feature subs...
Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular ...
AbstractFeature selection is one of the crucial steps in supervised learning, which influences the e...
AbstractFeature selection is a technique to choose a subset of variables from the multidimensional d...
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for...
Forward Selection (FS) is a popular variable selection method for linear regression. Working in a sp...
The task of identifying most relevant features for a credit scoring application is a challenging tas...
We address the feature subset selection problem for classification tasks. We examine the performance...
High dimensions of data cause overfitting in machine learning models, can lead to reduction in accur...
Classification Forward selection a b s t r a c t Most of the widely used pattern classification algo...
When dealing with high dimensional and low sample size data, feature selection is often needed to he...
In the wrapper approach to feature subset selection, a search for an optimal set of features is made...
<p>We propose a new binary classification and variable selection technique especially designed for h...
The identification of atypical observations and the immunization of data analysis against both outli...
When dealing with high dimensional and low sample size data, feature selection is often needed to he...
Microarray data usually contain a large number of genes, but a small number of samples. Feature subs...
Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular ...
AbstractFeature selection is one of the crucial steps in supervised learning, which influences the e...
AbstractFeature selection is a technique to choose a subset of variables from the multidimensional d...
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for...
Forward Selection (FS) is a popular variable selection method for linear regression. Working in a sp...
The task of identifying most relevant features for a credit scoring application is a challenging tas...
We address the feature subset selection problem for classification tasks. We examine the performance...
High dimensions of data cause overfitting in machine learning models, can lead to reduction in accur...
Classification Forward selection a b s t r a c t Most of the widely used pattern classification algo...
When dealing with high dimensional and low sample size data, feature selection is often needed to he...
In the wrapper approach to feature subset selection, a search for an optimal set of features is made...
<p>We propose a new binary classification and variable selection technique especially designed for h...
The identification of atypical observations and the immunization of data analysis against both outli...
When dealing with high dimensional and low sample size data, feature selection is often needed to he...
Microarray data usually contain a large number of genes, but a small number of samples. Feature subs...