Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …

Contemporary biological technologies produce extremely high-dimensional data sets from
which to design classifiers, with 20,000 or more potential features being common place. In …

The curse of dimensionality is based on the fact that high dimensional data is often difficult to
work with. A large number of features can increase the noise of the data and thus the error of …

An algorithm for filtering information based on the Pearson χ 2 test approach has been
implemented and tested on feature selection. This test is frequently used in biomedical data …

Feature selection is a central problem in machine learning and pattern recognition. On large
datasets (in terms of dimension and/or number of instances), using search-based or wrapper …

Feature selection, as a preprocessing step to machine learning, is effective in reducing
dimensionality, removing irrelevant data, increasing learning accuracy, and improving result …

Feature selection plays a significant role in improving the performance of the machine
learning algorithms in terms of reducing the time to build the learning model and increasing …

Feature selection can greatly enhance the performance of a learning algorithm when
dealing with a high dimensional data set. The filter method and the wrapper method are the …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Feature selection is a significant part of many machine learning applications dealing with
small-sample and high-dimensional data. Choosing the most important features is an …