Abstract
Compositional data have posed challenges to current classification methods owing to the non-negative and unit-sum constraints, especially when a certain of the components are zeros. In this paper, we develop an effective classification method for multivariate compositional data with certain of the components equal to zero. Specifically, a Kent feature embedding technique is first proposed to transform compositional data and improve data quality. We then use support vector machine as the state-of-the-art machine learning model to build the classifier. The proposed method is proved to be effective through numerical simulations. Results on multiple real datasets, including species classification, day-night image classification and household’s consumption pattern recognition, further verify that the proposed method can achieve good classification performance and outperform the other competitors. This method would help to broaden the practical usage of compositional data with zeros in the task of classification.
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Data are public available and details are given in the paper. Data can also be made available on reasonable request.
Notes
To explicitly showcase the proposed Kent feature embedding, the corresponding pseudocode is depicted in Algorithm 2, conveniently placed in the Appendix to maintain the paper’s conciseness.
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Funding
This study is funded by National Natural Science Foundation of China (Nos. 72371257, 72001222, 71873012). RG is partially supported by Humanities and Social Science General Program of the Ministry of Education of China (No. 23YJC910002). SL thanks the support from Jing Ying Scholar Support Program in Central University of Finance and Economics (CUFE) and is a member of Financial Sustainable Development Research Team in CUFE. SL, WW and RG also thank the support from Program for Innovation Research, the “Double First-Class” Disciplinary Project and the Disciplinary Funding in CUFE.
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SL: Conceptualization; Methodology; Formal analysis; Writing—original draft; Writing—review & editing. WW: Formal analysis; Writing—review & editing. RG: Conceptualization; Methodology; Writing—review & editing.
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Appendix
Kent feature embedding
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Lu, S., Wang, W. & Guan, R. Kent feature embedding for classification of compositional data with zeros. Stat Comput 34, 69 (2024). https://doi.org/10.1007/s11222-024-10382-z
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DOI: https://doi.org/10.1007/s11222-024-10382-z