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Cancer Classification of Gene Expression Data by Fast Clustering and Fuzzy TSVM
Abstract
Cancer classification using gene expression data usually relies on traditional supervised learning techniques, in which only labeled data can be exploited for learning. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer type existing system developed a classification system by identifying potential gene markers and subsequently applying the proposed technique on the selected genes for the classification of human cancer. In the proposed work, we use a fast clustering based feature selection technique and for classification we use a fuzzy based transductive support vector machine. By using the fast clustering based feature selection we can obtain the high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient Minimum-Spanning Tree (MST) clustering method. FTSVM method generates membership values iteratively based on the positions of training vectors relative to the TSVM decision surface itself.
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References
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