Abstract
The paper presents a comparative analysis of various types of normalization techniques. The accuracy of data classification which was carried out after data normalizing was used as the main criterion for evaluating the quality of the appropriate normalizing method. Four various types of datasets downloaded from the UCI Machine Learning Repository were used as the experimental data during the simulation process. Various normalization techniques available from package clusterSim of R software were applied to the experimental data. The quality of the data normalizing procedure was evaluated based on the use of data classification by the calculation of the accuracy of the objects distribution into classes. The neural network multilayer perceptron was used as the classifier at this step. The simulation results have shown that the data normalizing stage significantly influences the classification accuracy and selection of the normalization method depends on the type of data and, consequently, the selection of the normalizing technique should be carried out in each of the cases separately.
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Mishkov, O., Zorin, K., Kovtoniuk, D., Dereko, V., Morgun, I. (2022). Comparative Analysis of Normalizing Techniques Based on the Use of Classification Quality Criteria. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 77. Springer, Cham. https://doi.org/10.1007/978-3-030-82014-5_41
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