Feature (gene) selection and classification of microarray data are the two most interesting
machine learning challenges. In the present work two existing feature selection/extraction …

A novel method for microarray data classification is proposed in this letter. In this scheme,
the sequential floating forward selection (SFFS) technique is used to select the independent …

This paper proposed a new combination of feature selection/extraction approach for Artificial
Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an …

Microarray data play critical role in cancer classification. However, with respect to the
samples scarcity compared to intrinsic high dimensionality, most approaches fail to classify …

The explosion of the high-dimensional dataset in the scientific repository has been
encouraging interdisciplinary research on data mining, pattern recognition and …

Feature selection and classification are the main topics in microarray data analysis.
Although many feature selection methods have been proposed and developed in this field …

Microarray expression data analysis is critical for clinical treatment and biomedical research.
Although many research results have been reported in literatures, it is still very difficult to …

Independent component analysis (ICA) is a powerful concept for reducing the dimension of
big data in many applications. It has been used for the feature extraction of microarray gene …

With the rapid development of DNA microarray technology, large amount of genomic data
has been generated. Classification of these microarray data is a challenge task since gene …

Recently, various soft computing techniques have been used to extract k information from
big data. A standardized format for evaluating the expression levels of thousands of genes is …