Data normalization in machine learning

Abstract

In machine learning, the input data is often given in different dimensions. As a result of the scientific papers review, it is shown that the initial data described in different types of scales and units of measurement should be converted into a single representation by normalization or standardization. The difference between these operations is shown. The paper systematizes the basic operations presented in these scales, as well as the main variants of the function normalization. A new scale of parts is suggested and examples of the data normalization for correct analysis are given. Analysis of publications has shown that there is no universal method of data normalization, but normalization of the initial data makes it possible to increase the accuracy of their classification. It is better to perform data clustering by methods using distance functions after converting all features into a single scale. The results of classification and clustering by different methods can be compared with different scoring functions, which often have different ranges of values. To select the most accurate function, it is reasonable to normalize several functions and to compare their estimates on a single scale. The rules for separating features of tree-like classifiers are invariant to scales of quantitative features. Only comparison operation is used. Perhaps due to this property, the random forest classifier, as a result of numerous experiments, is recognized as one of the best classifiers in the analysis of data of different nature.

Keywords

• object classification
• clustering
• data normalization
• function normalization
• sigmoid
• hyperbolic tangent
• random forest