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<!DOCTYPE html>
<html lang="en">
<head>
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<title>UniFeat | Universal Feature Selection Tool</title>
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<meta name="description" content="UniFeat is an open-source tool developed in Java for performing feature selection in different areas of research. It provides a set of well-known feature selection methods.">
<meta name="keywords" content="UniFeat, Machine Learning, Feature Selection, High-dimensionality, Dimensionality Reduction, Supervised Learning, Unsupervised Learning">
<meta name="author" content="Sina Tabakhi">
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<h1>UniFeat</h1>
<h2>Universal Feature Selection Tool</h2>
<h6>An open-source tool for performing feature selection process</h6>
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<a href="software.html" class="btn btn-white-md">More</a>
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<h2>What is <span class="alternate">UniFeat</span>?</h2>
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<p>
The field of data mining is concerned with knowledge discovery from data through the
development of computer programs.
During the last two decades, the rapid advances in data acquisition capacity and
database technology have led to the production of datasets with large numbers of
features in many fields.
Most of the features are irrelevant and redundant, and these unnecessary features have
stimulated a phenomenon in the data mining methods.
Therefore, feature selection is an important and active research area in data
preprocessing.
Feature selection is the process of identifying a subset of relevant features in an
original feature set.
</p>
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<p>
Universal Feature Selection Tool (UniFeat) is an open-source tool entirely developed in
Java for performing feature selection process in different areas of research.
</p>
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<p>
UniFeat is principally implemented and developed at the
<a href="https://international.uok.ac.ir/en/" target="_blank">University of
Kurdistan</a>, Iran, and distributed under the
<a href="https://github.com/UniFeat/unifeat/blob/main/LICENSE" target="_blank">MIT License</a> terms.
</p>
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<div class="description-one">
<p>
UniFeat provides a set of well-known and state-of-the-art feature selection methods
within its practical auxiliary tools, including performance evaluation, results
visualization, statistical analysis, and reduced datasets to compare the performance of
feature selection methods.
</p>
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