If a dataset has multiple classes and huge features like microarray data, classification accuracy may be low, even though feature selections are applied to reduce the dimensions of the dataset. Improvement of classification accuracy for the dataset is a challenging task. We propose an efficient classification method based on the "Divide-and-Merge" approach for high dimensional multi-class datasets. In the proposed method, we extracted different feature subsets for each class in an original dataset and generate new datasets. Unknown sample Si is classified into the new datasets and the results are merged for a final decision of the class label.
展开▼
