Academic Journal of Mathematical Sciences, 2023, 4(5); doi: 10.25236/AJMS.2023.040501.
Xinyang Li, Donghao Li
Department of Mathematics, Henan University of Technology, Zhengzhou, China
This study explores the application of weighted majority voting combined with machine learning and artificial neural networks to the categorization of ancient glass artifacts. The study developed an optimized Multilayer Perceptron (MLP) classification model based on the cyclically optimized results from various well-performing classification models through weighted majority voting. The model demonstrated strong performance in cross-validation tests, achieving a prediction accuracy of 92.75%, demonstrating high stability and precision. This research provides a novel approach and methodology for the classification of ancient glass artifacts, potentially contributing to further advancement in this field.
Multilayer Perceptron, Ancient Glass Artifacts, Machine Learning, Weighted Majority Voting
Xinyang Li, Donghao Li. Multilayer Perceptron Classification Model Based on Weighted Majority Voting for Classifying Ancient Glass Products. Academic Journal of Mathematical Sciences (2023) Vol. 4, Issue 5: 1-8. https://doi.org/10.25236/AJMS.2023.040501.
[1] Wang C., Tao Y. The weathering of silicate glasses. Journal of Chinese Ceramic Society, 2003, 31(1):78-85.
[2] Li, F., Li, Q., Gan, F., Zhang, B., Cheng, H. Chemical composition analysis for some ancient chinese glasses by proton induced X-ray emission technique. Journal of Chinese Ceramic Society, 2005, 33, 581-586.
[3] Cui J., Wu X., Tan Y., et al. Chemical analysis of ancient glass wares unearthed from chu cemeteries of the warring state period in the drainage area of the Yuanshui river, Hunan province. Journal of Chinese Ceramic Society, 2009, 37(11): 1909-1913+1918.
[4] Fu Q., Zhao H., Dong J., et.al. Nondestructive analysis of the silicate artifacts excavated from Baofeng and Xinzheng, Henan province. Spectroscopy and Spectral Analysis, 2014, 34(01): 257-262.
[5] Huang X., Yan J., Wang H. Analysis of the decorated silicate beads excavated from tomb M4 of the Ma-Jia-Yuan warring states cemetery, Gansu province. Spectroscopy and Spectral Analysis, 2015, 35(10): 2895-2900.
[6] Rajakarunakaran S., Lourdu A., Lourdu A., et.al. Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Advances in Engineering Software, 2022, 173: 103267.
[7] Zheng K., Li X., Song S., Gao X. Discrimination of ginseng origin by using laser-induced breakdown spectrum and machine learning algorithms. Microwave and Optical Technology Letters, 2022, 65(5): 1248-1254.
[8] Lu J. Classifying model of ancient glass products based on ensemble feature selection and random forest. Journal of Chinese Ceramic Society, 2023, 51(04): 1060-1065.
[9] Tang H., Xu Y., Lin A., et.al. Predicting green consumption behaviors of students using efficient firefly grey wolf-assisted K-nearest neighbor classifiers. IEEE Access 2020, 8, 35546-35562.
[10] Chandra, T., Verma K., Singh B. Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Systems with Applications, 2021, 165: 113909.
[11] Chen Y., Zheng W., Li W., Huang Y. Large group activity security risk assessment and risk early warning based on random forest algorithm. Pattern Recognition Letters. 2021, 144: 1-5.
[12] Wu Y., Ke Y., Chen, Z., Liang S., et.al. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena, 2020, 187: 104396.
[13] M. Amjad, I. Ahmad, M. Ahmad, et al., Prediction of pile bearing capacity using XGBoost algorithm: modeling and performance evaluation, Appl. Sci. 2022, 12 (4): e01243.
[14] Gul A., Perperoglou A., Khan Z. et.al. Ensemble of a subset of kNN classifiers. Adv. Data Anal. Classif. 2018, 12, 827-840.
[15] Jiang Y., Xie J., Han Z. et.al. Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit immunomarker SVM-based predictive classifier. Clinical Cancer Research. 2018, 24, 5574-5584.
[16] Mohammadi B., Guan, Y., Moazenzadeh R., Safari M. Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation. Catena, 2021, 198: 105024.