Research Article
Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles
Input: | Dataset , sample size ; | Sample , number of total feature ; | Class of th sample in normal, tumor}; | Split function: yield training set and testing set from original dataset. If the original | dataset has been divided into training and testing partition, this step could be skipped. | Gene select function: , where is the feature number of selected data, ; | RS_preject function: , where is the size of a random subspace, ; | Number of random subspaces ; | Learning algorithm: SVM | Output: | Classification hypotheses : | Start: | Data processing: | (Trainset, Testset) = Split() | TrainsetNew = Gene_select(Trainset, ) | TestsetNew = Gene_select(Testset, ) | Generate and aggregate SVM classifiers on random subspaces: | For to | _project(TrainsetNew, ) | () | End | Test: | For each in TestsetNew | | End | End |
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