The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems.
The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. S2N is defined as |μ_{1} - μ_{2}| / (σ_{1} + σ_{2}), where μ_{1} and μ_{2} are the mean value of the variable in classes 1 and 2, respectively, and σ_{1} and σ_{2} are the standard deviations of the variable in classes 1 and 2, respectively. Clearly, features with larger S2N ratios are better for classification.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems.
The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems. For each variable j, this ratio is BSS(j) / WSS(j) = ΣI(y_{i} = k)(x_{kj} - x_{·j})^{2} / ΣI(y_{i} = k)(x_{ij} - x_{kj})^{2}; where x_{·j} denotes the average of variable j across all samples, x_{kj} denotes the average of variable j across samples belonging to class k, and x_{ij} is the value of variable j of sample i. Clearly, features with larger sum squares ratios are better for classification.
High level feature selection operators.