Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060724.
Kaikai Kang, Weijie Wu, Xianchun He
College of Computer and Data Science/College of Software, Fuzhou University, Fuzhou, 350108, China
Ancient glass artifacts are highly susceptible to weathering in burial environments, which can cause changes in their chemical composition. To address the problem of identifying and analysing the composition of ancient glass artifacts, this paper proposes a classification and identification model based on the Fisher discriminant method. Firstly, considering that the proportion of components in glass can change during the weathering process, which can affect the correct identification of its category, this paper predicts the data before weathering based on the normal distribution law and performs closed operations to ensure that the sum of the component data is 100%. Secondly, through variance analysis and kernel density estimation, the factors affecting the classification are selected, and a Fisher linear discriminant model is established. Finally, cross-validation is performed by applying Fisher linear discriminant to both the training and testing data, and the discriminant results of the two groups of data are found to be consistent with the actual situation, with a discrimination accuracy rate of 100%.
Identification of Ancient Glass Artifacts, Fisher Linear Discriminant, Kernel Density Estimation
Kaikai Kang, Weijie Wu, Xianchun He. Classification Model of Ancient Glass Artifacts Using Fisher Discriminant Analysis. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 182-187. https://doi.org/10.25236/AJCIS.2023.060724.
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