Abstract
With the development of microarray sequencing technology, researchers can obtain expression data of a large number of genes or proteins from patients at one time for analysis of biomarkers that cause disease. However, limited by the number of patient cohorts, the number of samples is usually small, so this type of data is often referred to as high-dimensional small sample data, also known as microarray data. In order to effectively select valid biomarkers, effective dimensionality reduction of the data is essential for further analysis. This paper proposes a two-stage feature selection method for data dimensionality reduction. The proposed method first improves two quantization functions of Max-Relevance and Min-Redundancy (mRMR) to make it applicable to microarray data for initial dimensionality reduction of the data. Subsequently, the improved genetic algorithm is used for further dimensionality reduction of the data. The proposed method combines the growth tree clustering algorithm with the genetic algorithm’s selection and crossover process to improve the crossover efficiency. In addition, we combine the feature recursive elimination module with the genetic algorithm iteration process for further dimensionality reduction of the data. The proposed method is demonstrated to be effective and advanced by conducting comparative experiments on four publicly available data.
Supported by Software foundation of the Ministry of industry and information technology of China (Grant No. 2105-370171-07-02-860873).
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Ji, Y., Li, J., Huang, Z., Xie, W., Zhao, D. (2022). A Data Dimensionality Reduction Method Based on mRMR and Genetic Algorithm for High-Dimensional Small Sample Data. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_43
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DOI: https://doi.org/10.1007/978-3-031-20309-1_43
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