Title :
Kernel classification rules from missing data
Author :
Pawlak, Miroslaw
Author_Institution :
Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnepeg, Man., Canada
fDate :
5/1/1993 12:00:00 AM
Abstract :
Nonparametric kernel classification rules derived from incomplete (missing) data are studied. A number of techniques of handling missing observation in the training set are taken into account. In particular, the straightforward approach of designing a classifier only from available data (deleting missing values) is considered. The class of imputation techniques is also taken into consideration. In the latter case, one estimates missing values and then calculates classification rules from such a completed training set. Consistency and speed of convergence of proposed classification rules are established. Results of simulation studies are presented
Keywords :
convergence; information theory; nonparametric statistics; imputation techniques; kernel classification rules; missing data; nonparametric classification rules; speed of convergence; training set; Cities and towns; Convergence; Equations; Kernel; Linear regression; Pattern recognition; Regression analysis; Sensor systems; Training data; Vectors;
Journal_Title :
Information Theory, IEEE Transactions on