FLDF Based Decision Tree using Extended Data Expression
Jong Chan Lee, Dong-Hun Seo, Chihwa Song, Won Don Lee · 2007
We introduce a classification algorithm which can be applied to a problem with a data set included a missing variable. In this algorithm we use data expansion treating it with a weight value and the probability techniques. It is applied to extending a classifier which is considered the optimal projection plane based on Fisher's formula. For doing this, we derive equations from the procedure to be applied to the data expansion. The result is compared to that of different measurements by choosing one variable in the data set and then modifying the rate of missing and non-missing values in this selected variable. The result of a data set with non-missing variable compares with that of C4.5 which is known as a knowledge acquisition tool in machine learning.