Feature selection methods target at selecting a subset of genes, whereas dimension reduction methods construct a small number of representative features (sometimes referred to as "super genes" or "latent genes" in the literature) using the linear combinations of all genes.
The general test case selection problem is concerned with selecting a subset of the test cases according to a specific (stop) criterion, whereas test suite reduction techniques focus on selecting a subset of the test cases, but the selected subset must provide the same requirement coverage as the original suite (Harrold et al. 1993).
Feature selection is a process of selecting a subset of relevant features available from the data that most contribute to distinguishing instances from different classes.
Feature selection refers to the problem of selecting a subset of the descriptors which can be used to build a model with optimal predictive ability [3].
The input-space feature selection reduces the dimensionality of data by selecting a subset of features to conduct a hypothesis testing or create a model under some selection criteria in the same space as input data (e.g., t-test).
Specifically, we develop adaptive link selection algorithms that can exploit the knowledge of poor links by selecting a subset of data from neighbor nodes.
The second step consists of selecting a subset of pixels according to their confidence measure.
The customization process is generally initiated by a configuration step, selecting a subset of the reference process model.
A comparative study is carried out in the problem of selecting a subset of basis functions in regression tasks.
Motivated by previous studies [4, 8], we also tried to maximize the correlation score by selecting a subset of genes.
Active learning attempts to address this issue by selecting a subset of most critical instances for labeling [58].
