High-dimensional classification methods have been a major target of machine learning for the automatic classification of patients who suffer from Alzheimer's disease (AD).
The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer's disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI).
It has been shown that regularization is very important when applying classification methods to high dimensional data, even for linear classifiers [61].
It has been shown that regularization seems to be very important when applying classification methods on high dimensional data [ 42].
As it is difficult to perceive high-dimensional spaces, a common way to illustrate classification methods is to use a 2D representation.
However, these classification methods suffer from the "curse of dimensionality" when applied to high-dimensional fault diagnosis data.
On the other hand, MRRMRR does not bring about benefit to classification methods which are based on one-dimensional principles.
To contrast the outcome of the GA/MLHD feature selection procedure with classification methods proposed to be not affected by the dimensional problem (p > > n), which is the case for microarray data analysis we applied also tree classifiers.
Comparing with other machine learning classification methods, SVM is more effective in high-dimensional space [ 15].
It shall be also noted that these classification methods can be also applied to high-dimensional data [ 45, 72].
The classifications of the independent set of samples determined by the two-dimensional classifiers were further validated by comparing with two other dengue classification methods: hemagglutination inhibition (HI) assay and an in-house anti-dengue IgG-capture ELISA method.
