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Machine learning for detecting gene-gene interactions: a review
Complex interactions among genes and environmental factors are known to play a role in
common human disease aetiology. There is a growing body of evidence to suggest that …
common human disease aetiology. There is a growing body of evidence to suggest that …
A weakly informative default prior distribution for logistic and other regression models
We propose a new prior distribution for classical (nonhierarchical) logistic regression
models, constructed by first scaling all nonbinary variables to have mean 0 and standard …
models, constructed by first scaling all nonbinary variables to have mean 0 and standard …
Gene selection with guided regularized random forest
The regularized random forest (RRF) was recently proposed for feature selection by building
only one ensemble. In RRF the features are evaluated on a part of the training data at each …
only one ensemble. In RRF the features are evaluated on a part of the training data at each …
Searching for interacting features in subset selection
The evolving and adapting capabilities of robust intelligence are best manifested in its ability
to learn. Machine learning enables computer systems to learn, and improve performance …
to learn. Machine learning enables computer systems to learn, and improve performance …
A novel feature selection method considering feature interaction
Z Zeng, H Zhang, R Zhang, C Yin - Pattern recognition, 2015 - Elsevier
Interacting features are those that appear to be irrelevant or weakly relevant with the class
individually, but when it combined with other features, it may highly correlate to the class …
individually, but when it combined with other features, it may highly correlate to the class …
Feature selection via regularized trees
We propose a tree regularization framework, which enables many tree models to perform
feature selection efficiently. The key idea of the regularization framework is to penalize …
feature selection efficiently. The key idea of the regularization framework is to penalize …
Feature interaction for streaming feature selection
Traditional feature selection methods assume that all data instances and features are known
before learning. However, it is not the case in many real-world applications that we are more …
before learning. However, it is not the case in many real-world applications that we are more …
Scalable and accurate online feature selection for big data
Feature selection is important in many big data applications. Two critical challenges closely
associate with big data. First, in many big data applications, the dimensionality is extremely …
associate with big data. First, in many big data applications, the dimensionality is extremely …
Machine learning based on attribute interactions
A Jakulin - 2005 - eprints.fri.uni-lj.si
Two attributes $ A $ and $ B $ are said to interact when it helps to observe the attribute
values of both attributes together. This is an example of a $2 $-way interaction. In general, a …
values of both attributes together. This is an example of a $2 $-way interaction. In general, a …
Gradient-based multi-label feature selection considering three-way variable interaction
Y Zou, X Hu, P Li - Pattern Recognition, 2024 - Elsevier
Abstract Nowadays, Multi-Label Feature Selection (MLFS) attracts more and more attention
to tackle the high-dimensional problem in multi-label data. A key characteristic of existing …
to tackle the high-dimensional problem in multi-label data. A key characteristic of existing …