Table 1.

Data pre-processing methods.

Category	Aim	Methods
Data cleaning	Handling of anomalies in data values	Missing value processing (abandon/replacement)
		Ectopic values processing
		Outlier and noise handling
Data integration	Increase sample data size	Combining multiple data sets into a single data set
Data standardization	Scales the sample values to a specified range	Discretization
		Dualization
		Normalization (min–max, z-score)
		Function transformation