To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features.
The objective of feature selection is to select a subset of features that gives as much information as possible.
Feature selection is designed to select a subset of features for avoiding the issue of 'curse of dimensionality'.
Feature selection is used to select a subset of relevant and non-redundant features from a large feature space.
Feature selection is used to select a subset of features relevant to the prediction of MoRF regions.
Commonly referred to as sensor selection, the basic objective is to select a subset of sensors from available sensors that minimises the estimation error.
Investigators must therefore select a subset of causal factors to include in the models they test.
The main idea is to select a subset of anchor nodes for location estimation.
For the evaluation of the approach we select a subset of the video collection.
We select a subset of shops in the dataset belonging to the same areas of Italy.
In this study we select a subset of four basic examples for comparison.
