Add to ORKGWe describe a feature selection method that can be applied directly to models that are linear with respect to their parameters, and indirectly to others. It is independent of the target machine. It is closely related to classical statistical hypothesis tests, but it is more intuitive, hence more suitable for use by engineers who are not statistics experts. Furthermore, some assumptions of classical tests are relaxed. The method has been used successfully in a number of applications that are briefly described
Abstract. We propose a new feature selection criterion not based on calculated measures between attr...
In view of the substantial number of existing feature selection algorithms, the need arises to coun...
The paper presents an algorithm to rank features in “small number of samples, large dimensionality” ...
We describe a feature selection method that can be applied directly to models that are linear with r...
We describe a feature selection method that can be applied directly to models that are linear with r...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Application of a feature selection algorithm to a textual data set can improve the performance of so...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Abstract: We presented a comparison between several feature ranking methods used on two real dataset...
The problem of feature selection for Support Vector Machines (SVMs) classification is investigated i...
We propose a new feature selection criterion not based on calculated measures between attributes, o...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
Abstract. We propose a new feature selection criterion not based on calculated measures between attr...
In view of the substantial number of existing feature selection algorithms, the need arises to coun...
The paper presents an algorithm to rank features in “small number of samples, large dimensionality” ...
We describe a feature selection method that can be applied directly to models that are linear with r...
We describe a feature selection method that can be applied directly to models that are linear with r...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Application of a feature selection algorithm to a textual data set can improve the performance of so...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Abstract: We presented a comparison between several feature ranking methods used on two real dataset...
The problem of feature selection for Support Vector Machines (SVMs) classification is investigated i...
We propose a new feature selection criterion not based on calculated measures between attributes, o...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
Abstract. We propose a new feature selection criterion not based on calculated measures between attr...
In view of the substantial number of existing feature selection algorithms, the need arises to coun...
The paper presents an algorithm to rank features in “small number of samples, large dimensionality” ...