JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (1): 14-28.doi: 10.6040/j.issn.1671-9352.4.2023.0212

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Fusing matrix factorization and space partition microbial data augmentation algorithm

WEN Liuying, WU Jun, MIN Fan   

  1. School of Computer and Software, Southwest Petroleum University, Chengdu 610500, Sichuan, China
  • Published:2025-01-10

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Abstract: Aiming at the problems of intra-class imbalance and inter-class imbalance and high sparsity of microbial data, a data augmentation method that fuses matrix factorization and space partition is proposed. Matrix factorization technology is used to decompose the original data space into object subspace and feature subspace to extract the latent space representation. The object subspace is divided into multiple data subspaces to alleviate the intra-class imbalance problem. Synthetic samples are then generated in each data subspace to address the inter-class imbalance. Synthetic samples are filtered using Euclidean distance to obtain high-quality samples. The experiment is conducted on 9 microbial data sets, and the performance is compared with 9 sampling algorithms. The results show that the samples generated by the proposed method have great advantages in diversity, and more positive samples can be identified under multiple classifiers.

Key words: matrix factorization, space partition, intra-class imbalance, inter-class imbalance, object subspace, feature subspace

CLC Number: 

  • TP391
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