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Classification of Tumor Gene Expression Data Based on Manifold Learning and Gaussian Process

Tumor gene expression data usually presents high-dimensional and imbalance characteristics. Effective feature dimension reduction has far-reaching significance for its classification. However, the real distribution of data in high-dimensional space is rarely considered in traditional feature extraction methods, which would lead to the distortion of the original data and the influence on classification accuracy of the minority class. A new method combining manifold learning and Gaussian process classification is proposed for gene expression data classification in this paper. Isometric Feature Mapping is used to reduce the dimension of target data and extract manifold features, then Gaussian process is trained to classify the tumor gene expression data. The experimental results on three real datasets show that the proposed method provides higher recognition accuracy, meanwhile, it is efficient and feasible for imbalanced data.