Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation
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Microarray gene expression data-based breast tumor classification is an active and challenging issue. In this paper, a robust breast tumor recognition framework is presented based on considering reducing clinical misdiagnosis rate and exploiting available information in existing samples. A wrapper gene selection method is established from a new perspective of reducing clinical misdiagnosis rate. The further feature selection of information genes is fulfilled by a modified NMF model, which is motivated by hierarchical learning and layer-wise pre-training strategy in deep learning. For completing the classification, an inverse projection sparse representation (IPSR) model is constructed to exploit information embedded in existing samples, especially in test ones. Moreover, the IPSR model is optimized via generalized ADMM and the corresponding convergence is analyzed. Extensive experiments on three public breast tumor datasets show that the proposed method is stable and effective for breast tumor classification. Compared to the latest literature, there is 14 higher in classification accuracy. Specificity and sensitivity achieve 94.17 and 97.5
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