From: Characteristics of predictor sets found using differential prioritization
Term | Meaning |
---|---|
| The value of the DDP leading to the best separation of classes in terms of the Euclidean distance |
| The value of the DDP leading to the best separation of classes in terms of the Manhattan distance in original feature space |
| The value of the DDP giving the largest antiredundancy in PC space |
| The value of the DDP leading to the best separation of classes in terms of the Manhattan distance in PC space |
α | A variable representing the DDP |
Antiredundancy | A parameter opposite to redundancy in terms of quality and thus is to be maximized along with relevance |
DDP | Degree of differential prioritization, which controls the balance between the two requirements in feature selection (maximizing relevance and maximizing antiredundancy) |
Equal-priorities scoring methods | Filter-based feature selection techniques in which the predictor set scoring method places equal importance on relevance and redundancy as criteria in forming the predictor set |
G | Number of genes in each group of marker genes, a parameter set during the generation of toy datasets |
K | Number of classes in the dataset |
m | Class size (number of samples per class), a parameter set during the generation of toy datasets |
N | Number of genes in the dataset |
OVA | One-vs.-all |
P | Predictor set size, i.e., number of genes selected into the predictor set |
PCA | Principal component analysis |
PW | Pairwise |
Rank-based selection or rank-based techniques | Filter-based feature selection techniques in which relevance is the sole criterion in forming the predictor set |
Redundancy | The redundancy in a predictor set indicates the amount of similarity among the members of the predictor set |
Relevance | The ability to distinguish among different classes |
S α | The predictor set found using a DDP value of α |