Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Support vector machine has emerged as one of the most crucial classification techniques in recent years in pattern recognition, machine learning, and data mining. The model training will become more challenging when dealing with multi-classification problems, and its training time will increase considerably as the number of samples increases. A quick training data reduction technique MOIS that works with multiple classification problems is provided to address the issues above. This method takes the cluster centre as the reference point. While removing redundant training samples, selects the decisive boundary samples to significantly reduce the training data and reduce the distribution imbalance between categories. Experimental results show that MOIS can maintain or even improve the classification effect of support vector machines. Moreover, it can significantly improve training efficiency. For example, on the Opt digit data set, the proposed method improves the classification accuracy from 98.94% to 99.05% and, at the same time, shortens the training time to 15%; On the data set composed of the first 100 categories, when the accuracy rate is slightly improved (from 99.29% to 99.30%), the training time is significantly shortened to less than 6% of the original. In addition, MOIS itself has High operating efficiency.