Analysis of Time-Dependencies in Automatic Security Classification
Paal Einar Engelstad, Hugo L. Hammer, Anis Yazidi, Aleksander Bai · 2015
Research that explores the use of machine learning for automatic security classification of information objects is about to emerge. In this paper we investigate the opportunity to increase the machine learning performance by taking advantage from time information that is "hidden" in the documents of the training set. This paper presents a technique to do so, and confirms that this is a promising way to improve performance. Furthermore, various time functions are investigated to prepare the ground for further work into this promising area. To the best of our knowledge there exist no publications using these kind of time dependent interactions to the bag-of-words approach that is commonly used for text analysis of documents.