QLMC-HD: Quasi Large Margin Classifier based on Hyperdisk
This is an incremental improvement for data classification, specifically addressing robustness to noise in large margin classifiers.
The paper tackles the problem of noisy data in classification by optimizing a large margin classifier based on hyperdisk and combining it with a quasi-support vector data description method, resulting in a more robust and efficient classifier with the widest margin compared to others.
In the area of data classification, the different classifiers have been developed by their own strengths and weaknesses. Among these classifiers, we propose a method that is based on the maximum margin between two classes. One of the main challenges in this area is dealt with noisy data. In this paper, our aim is to optimize the method of large margin classifiers based on hyperdisk (LMC-HD) and combine it into a quasisupport vector data description (QSVDD) method. In the proposed method, the bounding hypersphere is calculated based on the QSVDD method. So our convex class model is more robust compared with the support vector machine (SVM) and less tight than LMC-HD. Large margin classifiers aim to maximize the margin and minimizing the risk. Since our proposed method ignores the effect of outliers and noises, so this method has the widest margin compared with other large margin classifiers. In the end, we compare our proposed method with other popular large margin classifiers by the experiments on a set of standard data which indicates our results are more efficient than the others