Balanced data sets for classification issues are special classes, and class distribution between classes is not uniform. In general, they are created by two types: the majority (negative) class and minority (positive) class.
Because this type of set data mining is a new challenging problem, because standard classification protocols typically considers a consistent training package and most of it is thinking of a pro in class.
