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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 |μ1 - μ2| / (σ1 + σ2), where μ1 and μ2 are the mean value of the variable in classes 1 and 2, respectively, and σ1 and σ2 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(yi = k)(xkj - x·j)2 / ΣI(yi = k)(xij - xkj)2; where x·j denotes the average of variable j across all samples, xkj denotes the average of variable j across samples belonging to class k, and xij 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.