Incremental Maximum Gaussian Mixture Partition For Classification (original) (raw)

In the field of classification, the main task of most algorithms is to find a perfect decision boundary. However, most decision boundaries are too complex to be discovered directly. Therefore, in this paper, we proposed an Incremental Maximum Gaussian Mixture Partition (IMGMP) algorithm for classification, aiming to solve those problems with complex decision boundaries. As a self-adaptive algorithm, it uses a divide and conquer strategy to calculate out a reasonable decision boundary by step. An Improved K-means clustering and a Maximum Gaussian Mixture model are used in the classifier. This algorithm also has been tested on artificial and real-life datasets in order to evaluate its remarkable flexibility and robustness.