Repozytorium publikacji - Politechnika Gdańska

This paper discusses the possibility of designing a two stage classifier for large-scale hierarchical and multilabel text classification task, that will be a compromise between two common approaches to this task. First of it is called big-bang, where there is only one classifier that aims to do all the job at once. Top-down approach is the second popular option, in which at each node of categories’ hierarchy, there is a flat classifier that makes a local classification between categories that are immediate descendants of that node. The article focuses on evaluating the performance of a k-NN algorithm at different levels of categories’ hierarchy, aiming to test, whether it will be profitable to make a semi-big-bang step (restricted to a specified level of the hierarchy), followed by a middle-down more detailed classification. Presented empirical experiments are done on Simple English Wikipedia dataset.

Autorzy

• mgr inż. Karol Draszawka link otwiera się w nowej karcie ,
• dr hab. inż. Julian Szymański link otwiera się w nowej karcie