A New Algorithm for Conformity and Its Application

Abstract

This article proposed a new algorithm of conformity using original data to calculate similarities between the target ob- ject and the expected value based on the Mahalanobis distance, providing an objective and reasonable analysis. Firstly, simulation experiments were conducted to obtain Mahalanobis distance (d) related to number (p) of different variables (traits) and similarity (r). Then, a surface fitting method was used to establish the function relationship between conformity (r) and index number (p), as well as Mahalanobis distance (d). Monte Carlo experiment for frequency distribution of conformity verified its good performance of the relationship model. The simulation results fully validated the feasibility and reliability of the model. Conformity algorithm was applied to calculate the similarity of a panel of Yangmai wheat varieties released in recent years referring to RVA parameters. The assessment of simulated multivariate regression for complex effects was also conducted. This study showed that conformity algorithm using raw data directly instead of standardized data reduces the work load and decreases inconsistency in similarity assessment with different data processing methods. In addition, conformity algorithm does not need weight assignment to each trait, thus can eliminate potential subjective impacts on traits or data and guarantee integrity of information and reliability of