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However, as with any method, sound experimental design is essential to generate robust results from microarray experiments, particularly given the issues of high dimensionality [ 3].
In order to address the issues of high dimensionality, the feature filter methods, such as the DF, GI, CDM, and Acc2, are utilized to select the most important features based on document frequency.
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In the literature, many methods have been developed to tackle the issue of high dimensionality of HS data[5].
Current experimental research in several scientific areas must deal with the issue of high dimensionality and complexity.
The authors of [84] proposed the hybrid deep learning model, called DisBelief, to address the issue of high dimensionality in big data streams.
Since higher dimensional feature vectors contain several irrelevant features that reduce the performance of pattern recognition methods, feature selection by itself can be used in most of the modern data classification methods to combat the issues resulting from the curse of high dimensionality [18, 19].
The data provided in the NCI-DREAM data-sets are of high dimensionality (contain a large number of features).
Usually, the image space is always of high dimensionality.
However, there are many strategies to mitigate the statistical consequences of high dimensionality.
The estimated motion energy field M u, v, t) is of high dimensionality.
However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process.
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