Noise control in document classification based on fuzzy formal concept analysis
Sheng-Tun Li, Fu-Ching Tsai · 2011
Document classification is critical due to explosive increasing of text in modern world. However, most of existing document classification algorithms are easily affected by noise data. Therefore, in document classification tasks, the ability of noise control is as important as the ability to classify exactly. In this paper, we propose a novel classification framework based on fuzzy formal concept analysis to moderate the impact from noise. In addition, the well-organized concepts also provide inherent relations, which support knowledge codification and distribution effectively. Experimental results using Reuters 21578 dataset demonstrates significant noise control benefit and superior classification accuracy.