Add to ORKGIn view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several fundamental algorithms found in the literature and assesses their performance in a controlled scenario. A scoring measure ranks the algorithms by taking into account the amount of relevance, irrelevance and redundance on sample data sets. This measure computes the degree of matching between the output given by the algorithm and the known optimal solution. Sample size effects are also studied.Postprint (published version
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
We propose a new feature selection criterion not based on calculated measures between attributes, or...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
In view of the substantial number of existing feature selection algorithms, the need arises to coun...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
AbstractFeature selection, as a dimensionality reduction technique, aims to choosing a small subset ...
Summarization: Feature selection (FS) is a significant topic for the development of efficient patter...
One major component of machine learning is feature analysis which comprises of mainly two processes:...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
AbstractFeature selection is an effective technique in dealing with dimensionality reduction. For cl...
In this paper, we propose a new feature selection criterion. It is based on the projections of data ...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
We propose a new feature selection criterion not based on calculated measures between attributes, or...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
In view of the substantial number of existing feature selection algorithms, the need arises to coun...
In view of the substantial number of existing feature selection algorithms, the need arises to count...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
Feature selection is a term standardin data mining to reduce inputs to a manageable size for analysi...
AbstractFeature selection, as a dimensionality reduction technique, aims to choosing a small subset ...
Summarization: Feature selection (FS) is a significant topic for the development of efficient patter...
One major component of machine learning is feature analysis which comprises of mainly two processes:...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
AbstractFeature selection is an effective technique in dealing with dimensionality reduction. For cl...
In this paper, we propose a new feature selection criterion. It is based on the projections of data ...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
Machine learning algorithms automatically extract knowledge from machine readable information. Unfor...
We propose a new feature selection criterion not based on calculated measures between attributes, or...