Artificial Intelligence

Methodology for Normalizing Computer Hardware Performance to a Unified Scale Based on Cross-Benchmark Analysis

Authors: Mikhail N. Velikanov, Igor V. Sedykh

This paper presents a comparative analysis of seven normalization methods (Min-Max, Z-score, Robust, Rank, Logarithmic, Reference-based) for aligning CPU performance scores across different benchmarks (UserBenchmark, PassMark). Using a dataset of 23 CPU models present in both sources, we evaluate methods using MAE, RMSE, MAPE, and correlation coefficients. Logarithmic Scaling achieves the best accuracy (MAE=0.034, MAPE=4.20%), while Rank Transformation best preserves component ordering (Spearman's ρ=0.959). All methods demonstrate high correlation (Pearson > 0.89), confirming the feasibility of effective cross-benchmark alignment. The proposed methodology can improve automated PC configuration recommendation systems.

Comments: 16 Pages. [In Russian] 6 figures, code available on GitHub

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Submission history

[v1] 2026-05-26 02:17:31

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