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For microarray data classification problem, selecting relevant genes from microarray data pose a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved and the usually small sample size. In order to correctly analyze microarray data, the goal of feature (gene) selection is to select those subsets of differentially expressed genes that...
In recent years, many studies have shown that microarray gene expression data is useful for disease identification and cancer classification. However, since gene expression data may contain thousands of genes simultaneously, successful microarray classification can be rather difficult. Feature (gene) selection is a frequently used pre-processing technology for successful classification of microarray...
Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. Compared to the number of genes involved available training data sets generally have a fairly small sample size in cancer type classification. These training data limitations constitute a challenge to certain classification methodologies. The gene (feature) selection...
Many previous research papers have demonstrated that microarray gene expression data are useful for disease classification and medical diagnosis. Cancer microarray data normally have a particular characteristic where features (genes) greatly exceed the instance (tissue sample) numbers. Selecting appropriate numbers and relevant features to differentiate different types of cancer remains a challenge...
Feature selection is a useful pre-processing technique for solving classification problems. The challenge of using evolutionary algorithms lies in solving the feature selection problem caused by the number of features. Classification data may contain useless, redundant or misleading features. To increase the classification accuracy, the primary objective is to remove irrelevant features in the feature...
In this paper, a novel grey-based feature ranking method for feature subset selection is proposed. Experiments performed on various application domains are reported to demonstrate the performance of the proposed approach. It can be easily seen that the proposed approach yields high performance and is helpful for pattern classification
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