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Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the...
Data mining algorithms are commonly used for cancer classification. Prediction models were widely used to classify cancer cells in human body. This paper focuses on finding small number of genes that can best predict the type of cancer. From the samples taken from several groups of individuals with known classes, the group to which a new individual belongs to is determined accurately. The paper uses...
Microarray data analysis based on gene expression profiles is attracting more and more attention from researchers for finding functional genes and for classifying diseases. Various available approaches for selecting features and for classification can be exploited to manipulate such data. However, fewer methods can be elegantly adapted to accomplish this purpose. The main challenge is that such microarray...
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