Authors :
KONE Dramane; GOORE Bi Tra; Dr. KIMOU Kouadio Prosper
Volume/Issue :
Volume 5 - 2020, Issue 11 - November
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3qALC2V
Abstract :
In this paper, we present a hybrid method for
efficiently estimating missing discrete attributes
appearing in data manipulation or processing. The
principle of the method consists first of all in
determining the segment to which the missing value
belongs and then estimating it by majority vote when
possible. Otherwise the average of the missing attribute
is determined from the complete data of the segment.
Several cases may arise. The case where the non-missing
attributes have the same modality (they are in the same
interval) is dealt with by calculating the centre of the
missing attribute
Keywords :
Cleaning, Estimation, Segmentation, Classification, MAR, Data Mining
In this paper, we present a hybrid method for
efficiently estimating missing discrete attributes
appearing in data manipulation or processing. The
principle of the method consists first of all in
determining the segment to which the missing value
belongs and then estimating it by majority vote when
possible. Otherwise the average of the missing attribute
is determined from the complete data of the segment.
Several cases may arise. The case where the non-missing
attributes have the same modality (they are in the same
interval) is dealt with by calculating the centre of the
missing attribute
Keywords :
Cleaning, Estimation, Segmentation, Classification, MAR, Data Mining
