Combining negative selection with immune K-means algorithm for improving the support vector machines method

This paper presents a novel method of using the ideas from Artificial Immune Systems for improving the performance of the support Vector Machines. By means of Immune K-Means algorithm a set of artificial data is generated based on the oryginal training data. The artificial data describes the most important information from the classifiers learning point of view - the information about the boundaries among the classes remain in the artificial data. Combining the Immune K-Means algorithm with Negative Selection methods allows for further improvements of the artificial data set. The proposed approach allows to speed up the learning process of SVM when the training data set is large by extracting the most important information first. The proposed method can also be used as a data compression, especially suited when the information about boundaries among classes is an important issue. The artificial data can be created once and then used for parameters tuning of different classification methods, speeding up the learning process.