Dimension Reduction and Classifier-Based Feature Selection for Oversampled Gene Expression Data and Cancer Classification

Gene expression data are usually known for having a large number of features. Usually, some these features irrelevant and redundant. However, in cases, all features, despite being numerous, show high importance contribute to the analysis. In similar fashion, gene sometimes have limited instances with rate imbalance among classes. This can limit exposure classification model different categories, thereby influencing performance model. this study, we proposed cancer detection approach that utilized preprocessing techniques such as oversampling, feature selection, models. The study used SVMSMOTE oversampling six examined datasets. Further, selection using dimension reduction methods classifier-based ranking selection. We trained machine learning algorithms, repeated 5-fold cross-validation on microarray algorithms differed based technique used.