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Minimum redundancy feature selection

Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
Parkinson’s disease is a complex chronic neurodegenerative disorder of the central nervous system. One of the common symptoms for… Expand
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2020
2020
Railcar condition is an important factor in the complex web of relationships between railroads, railcar leasing companies… Expand
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2019
2019
In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of… Expand
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2016
2016
In this paper, we use multilayer Perceptron model and a supervised learning technique called backpropagation to train a neural… Expand
2015
2015
Abstract Dimensionality reduction is an important and challenging task in machine learning and data mining. It can facilitate… Expand
2015
2015
In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast… Expand
Highly Cited
2011
Highly Cited
2011
This paper presents a hybrid filter-wrapper feature subset selection algorithm based on particle swarm optimization (PSO) for… Expand
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2010
2010
We present in this paper a comprehensive analysis of the mutual information based feature selection algorithms. We point out the… Expand
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2010
2010
Finding relevant subspaces in very high-dimensional data is a challenging task not only for microarray data. The selection of… Expand
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Highly Cited
2005
Highly Cited
2005
How to selecting a small subset out of the thousands of genes in microarray data is important for accurate classification of… Expand