Abstract
Oxides are classified into various oxides according to their constituent elements, among which there are binary and quaternary oxides. Binary oxides are widely used in industry, and quaternary oxides have great potential in this aspect of electrode materials. This paper focuses on a support vector machine based classification method for oxides, redefining the method for describing metal oxides in order to facilitate computation, and using principal component analysis to reduce the dimensionality of the data, evaluating the classification results using a variety of metrics, and comparing the classification results of multiple kernel functions and the number of principal components to select the best algorithm. The results show that the support vector machine with the number of principal components of 4 and a Gaussian kernel function has better classification results and is important for the identification of oxides.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jorner, K., Tomberg, A., Bauer, C., Skӧld, C., Norrby, P.-O.: Organic reactivity from mechanism to machine learning, Nat. Rev. Chem., 5(4), 240–255 (2021). 39 S
Lu, Q., Zhou, Y., Ouyang, Y., Guo, Q., Li, Wang, J.: Accelerated discovery of stable lead-free hybrid organicinorganic perovskites via machine learning. Nat. Commun., 9(1), 3405 (2018)
Ghiringhelli, L.M., Vybiral, J., Levchenko, S.V., Draxl, C., Scheffler, M.: Big data of materials science: critical role of the descriptor. Phys. Rev. Lett. 114(10), 105503 (2015)
Cortes, C., Vapnik, V.N.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
Breiman, L.: Random Forest. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Wang, Y.-C., Wang, Y., Yang, Z.-X., et al.: Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context. BMC Syst. Biol. 5(S1), S6 (2011)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Wei, L., Xing, P., Zeng, J., Chen, J., Su, R., Guo, F.: Improved prediction of protein–protein interactions using novel negative samples, features, and an ensemble classifier. Artif. Intell. Med. 83, 67–74 (2017)
Wei, L., Xing, P., Su, R., Shi, G., Ma, Z.S., Zou, Q.: CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J. Proteome Res. 16(5), 2044–2053 (2017)
Hu, Y., Zhao, T., Zhang, N., Zang, T., Zhang, J., Cheng, L.: Identifying diseases-related metabolites using random walk. BMC Bioinformat. 19(5), 37–46 (2018)
Zhang, M., et al.: MULTiPly: a novel multi-layer predictor for discovering general and specific types of promoters. Bioinformatics 35(17), 2957–2965 (2019)
Song, T., RodrÃguez-Patón, A., Zheng, P., Zeng, X.: Spiking neural P systems with colored spikes. IEEE Trans. Cogn. Dev. Syst. 10(4), 1106–1115 (2017)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61902337), Xuzhou Science and Technology Plan Project (KC21047), Jiangsu Provincial Natural Science Foundation (No. SBK2019040953), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 19KJB520016) and Young Talents of Science and Technology in Jiangsu and ghfund202302026465.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xiao, K., Wang, Z., Bao, W. (2023). Metal Oxide Classification Based on SVM. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_59
Download citation
DOI: https://doi.org/10.1007/978-981-99-4755-3_59
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4754-6
Online ISBN: 978-981-99-4755-3
eBook Packages: Computer ScienceComputer Science (R0)