Semi-Supervised Self-Organizing Feature Map for Gene Classification
نویسندگان
چکیده
In this thesis, a study on gene expression data analysis is done using some supervised, unsupervised and semi-supervised approaches. The task of class prediction for six gene expression datasets (namely, Brain Tumor, Colon Cancer, Leukemia, Lymphoma and SRBCT) has been carried out. Here, a one-dimensional self-organizing feature maps (SOFM) in a semi-supervised learning framework is developed for class prediction. Iterative learning of the SOFM network is carried out using a few labeled patterns along with some selected (i.e., most confident) unlabeled patterns. Comparative analysis of five algorithms (i.e., K-Means, Constrained K-Means, k-Nearest Neighbour, SOFM and a proposed algorithm Semi-supervised SOFM) is done. The results are found to be encouraging for the semi-supervised approaches in the domain of gene expression data analysis when a few labeled patterns are available.
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