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|Title:||Designing relevant features for continuous datasets using ICA|
|Citation:||International Journal of Computational Intelligence and Applications, 2008; 7(4):447-468|
|Publisher:||Imperial College Press|
|Mithun Prasad, Arcot Sowmya and Inge Koch|
|Abstract:||Isolating relevant information and reducing the dimensionality of the original data set are key areas of interest in pattern recognition and machine learning. In this paper, a novel approach to reducing dimensionality of the feature space by employing independent component analysis (ICA) is introduced. While ICA is primarily a feature extraction technique, it is used here as a feature selection/construction technique in a generic way. The new technique, called feature selection based on independent component analysis (FS_ICA), efficiently builds a reduced set of features without loss in accuracy and also has a fast incremental version. When used as a first step in supervised learning, FS_ICA outperforms comparable methods in efficiency without loss of classification accuracy. For large data sets as in medical image segmentation of high-resolution computer tomography images, FS_ICA reduces dimensionality of the data set substantially and results in efficient and accurate classification.|
|Keywords:||Feature subset selection|
independent component analysis
|Appears in Collections:||Aurora harvest 5|
Mathematical Sciences publications
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