Asymptotic Properties of Distance-Weighted Discrimination
نویسندگان
چکیده
While Distance-Weighted Discrimination (DWD) is an appealing approach to classification in high dimensions, it was designed for balanced data sets. In the case of unequal costs, biased sampling or unbalanced data, there are major improvements available, using appropriately weighted versions of DWD. A major contribution of this paper is the development of optimal weighting schemes for various nonstandard classification problems. The second major contribution is substantial asymptotic study of both the original and the weighted DWD. Let n be the sample size and d be the dimension of data. Both conventional (n-asymptotic) Fisher consistency and high dimension low sample size asymptotics (d-asymptotics) are studied.
منابع مشابه
Weighted Distance Weighted Discrimination and Its Asymptotic Properties.
While Distance Weighted Discrimination (DWD) is an appealing approach to classification in high dimensions, it was designed for balanced datasets. In the case of unequal costs, biased sampling, or unbalanced data, there are major improvements available, using appropriately weighted versions of DWD (wDWD). A major contribution of this paper is the development of optimal weighting schemes for var...
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