MaaniBeigy/fuzzyrescaler: Fuzzy feature scaling methods

Feature scaling is a method used to normalize the range of variables or features of data. In data processing, it is also known as data normalization and is usually performed during the data preprocessing step. With the fuzzy feature scaling/transformation, some useful knowledge may be extracted from features, and the new rescaled vectors may tolerate the possible uncertain information (such as outlier values, and rescaling of categorical variables). Also, the classification methods can achieve better accuracies using Fuzzy feature scaling methods. References are Juszczak, Tax, & Duin (2002) <doi:10.1.1.100.2524> , Jin, Tang, & Zhang (2007) <doi:10.1016/j.ins.2006.03.015> .

install.packages("remotes")
remotes::install_github("MaaniBeigy/fuzzyrescaler")