From Data and Information Analysis to Knowledge Engineering
DOI: 10.1007/3-540-31314-1_14
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Input Variable Selection in Kernel Fisher Discriminant Analysis

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“…Input variable selection in linear DFA has been shown to produce simpler models with improved predictive accuracy that would otherwise be hindered by the presence of redundant variables . In this study, we opted to base the variable selection process by producing a cutoff in the univariate analyses test statistic, ie, the independent samples t test.…”
Section: Methodsmentioning
confidence: 99%
“…Input variable selection in linear DFA has been shown to produce simpler models with improved predictive accuracy that would otherwise be hindered by the presence of redundant variables . In this study, we opted to base the variable selection process by producing a cutoff in the univariate analyses test statistic, ie, the independent samples t test.…”
Section: Methodsmentioning
confidence: 99%