Acquisition of a classification model for a risk search system from unbalanced textual examples
Shigeaki Sakurai, Ryohei Orihara · International Journal of Business Intelligence and Data Mining · 2009
This paper proposes a method that acquires a more appropriate classification model for a risk search system analysing corporate reputation information included in bulletin board sites. The method inductively acquires the model from textual examples composed of many negative examples and a few positive examples. It selects two kinds of important negative examples by referring to expressions related to a specific label. Here, the label represents the contents of the papers. Finally, the method uses the selected negative examples and all the positive examples to acquire the model. The paper verifies the effectiveness of the method through comparative experiments.