F score

F score

1. "Features with high quality should assign similar values to instances in the same class and different values to instances from different classes" - Ref: Data Classification Algorithms and Applications

2. "The key idea of Fisher score is to find a subset of feafeatures, such that in the data space spanned by the selected features, the distances between data points in different classes are as large as possible, while the distances between data points in the same class are as small as possible" - Ref: Generalized Fisher Score for Feature Selection

3. "To alleviate the difficulty, the widely used heuristic strategy is to compute a score for each feature independently according to the criterion F" - Ref: Generalized Fisher Score for Feature Selection

4. "This criterion is intuitively appealing, because it is easier to tell the classes apart using variables (functions of variables) for which the between-groups sum of squares is large relative to the within-groups sum of squares" - Ref: Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data

5. "Since this score is computed independently, the features selected may represent a suboptimal set. Further- more, this heuristic may fail to select redundant features or those with a high aggregated discriminative power" - Ref: Fisher Score-Based Feature Selection for Ordinal Classification: A Social Survey on Subjective Well-being