Tuning N-gram String Kernel SVMs via Meta Learning

Abstract

Even though Support Vector Machines (SVMs) are capable of identifying patterns in high dimensional kernel spaces, their performance is determined by two main factors: SVM cost parameter and kernel parameters. This paper identifies a mechanism to extract meta features from string datasets, and derives a n-gram string kernel SVM optimization method. In the method, a meta model is trained over computed string meta-features for each dataset from a string dataset pool, learning algorithm parameters, and accuracy information to predict the optimal parameter combination for a given string classification task. In the experiments, the n-gram SVM were optimized using the proposed algorithm over four string datasets: spam, Reuters-21578, Network Application Detection and e-News Categorization. The experiment results revealed that the proposed algorithm was able to produce parameter combinations which yield good string classification accuracies for n-gram SVM on all string datasets.