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Cancers are a large family of diseases that involve abnormal cell growth with the potential to spread to other parts of the body. A cancer disease in any of its forms represents a major cause of death worldwide. In cancer diagnosis, classification of different tumor types is of the greatest significance. Accuracy for prediction of various tumor types gives better treatment and minimization of toxicity...
Hyper networks consist of a large number of hyper edges that represent high-order features sampled from training sets. The order of hyper edges is an important parameter of a hyper network model and influences the performance of the hyper network classification system. Previous studies determine the parameter by the artificial exhaustive search method before evolutionary learning. Not only is the...
Microarray data contains thousands of genes which are used to evaluate expression level. However, most of them are not associated with cancer diseases and leads to the curse of dimensionality. The challenge based on microarray data is feature selection which searches for subsets of informative genes. At the moment, these techniques focus on filter and wrapper approaches to discover subsets of genes...
The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage...
Gene expression data usually contains a large number of genes (several thousand or more) but a small number of samples (usually <100). Among all the genes, many are irrelevant, insignificant or redundant to the discriminant problem under investigation. Hence the identification of informative genes, which have the greatest power for classification, is of fundamental and practical importance to the...
This work presents an algorithm for generating the GA-based (Genetic Algorithm) classifier for microarray data classification. The microarray dataset comprises of a small number of samples with very high features. In order to construct the GA-based classifier, a number of informative features (genes) are selected. These features are divided into 2 groups (10 features or less in each group). The summation...
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