EXTENDING DECISION TREE CLASIFIERS FOR UNCERTAIN DATA
M. Suresh Krishna Reddy, Ramapurath S. Jayasree · 2012
Traditionally, decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arise s in many applications during the data collection p rocess. Example sources of uncertainty include measurement/quantization errors , data staleness, and multiple repeated measurement s. With uncertainty, the value of a data item is often represented not by on e single value, but by multiple values forming a pr obability distribution. Rather than