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Breast Tumor Classification Based on Decision Information Genes and Inverse Projection Sparse Representation
Apr 17, 2018
Microarray gene expression data-based breast tumor classification is an active and challenging issue. In this paper, a robust framework of breast tumor recognition is presented aiming at 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 achieved using the modified NMF model, which is rooted in the use of 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 the test ones. Moreover, the IPSR model is optimized through generalized ADMM and the corresponding convergence is analyzed. Extensive experiments on public microarray gene expression datasets show that the proposed method is stable and effective for breast tumor classification. Compared to the latest open literature, there is 14% higher in classification accuracy. Specificity and sensitivity achieve 94.17% and 97.5%, respectively.
Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages
Dec 07, 2016
Various kinds of k-nearest neighbor (KNN) based classification methods are the bases of many well-established and high-performance pattern-recognition techniques, but both of them are vulnerable to their parameter choice. Essentially, the challenge is to detect the neighborhood of various data sets, while utterly ignorant of the data characteristic. This article introduces a new supervised classification method: the extend natural neighbor (ENaN) method, and shows that it provides a better classification result without choosing the neighborhood parameter artificially. Unlike the original KNN based method which needs a prior k, the ENaNE method predicts different k in different stages. Therefore, the ENaNE method is able to learn more from flexible neighbor information both in training stage and testing stage, and provide a better classification result.