Breast Density Classification Using Multiple Feature Selection

Breast Density Classification Using Multiple Feature Selection

Mammography as an x-ray method usually gives good results for lower density breasts while higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms knowledge about breast density can be useful for setting an appropriate decision threshold in order to produce more accurate detection. Because the...

Unsupervised Feature Selection Using Feature Density Functions

Since dealing with high dimensional data is computationally complex and sometimes even intractable, recently several feature reductions methods have been developed to reduce the dimensionality of the data in order to simplify the calculation analysis in various applications such as text categorization, signal processing, image retrieval, gene expressions and etc. Among feature reduction techniq...

Feature Selection Using Classification Methods

Feature selection using genetic algorithm for classification of schizophrenia using fMRI data

In this paper we propose a new method for classification of subjects into schizophrenia and control groups using functional magnetic resonance imaging (fMRI) data. In the preprocessing step, the number of fMRI time points is reduced using principal component analysis (PCA). Then, independent component analysis (ICA) is used for further data analysis. It estimates independent components (ICs) of...

Feature Selection using Multiple Streams

Feature selection for supervised learning can be greatly improved by making use of the fact that features often come in classes. For example, in gene expression data, the genes which serve as features may be divided into classes based on their membership in gene families or pathways. When labeling words with senses for word sense disambiguation, features fall into classes including adjacent wor...