Crowd-based Feature Selection for Document Retrieval in Highly Demanding Decision-making Scenarios

Automatic dimensionality reduction in text classification requires large training data sets due to the high dimensionality of the native feature space. However, in several real world multi-label problems, such as highly demanding decision-making scenarios, to manually classify and select features in large document sets is usually unfeasible even by specialist teams. This paper presents CrowdFS a first approach on using collective intelligence techniques to select label specific relevant features from a large document set. An experiment in the context of competitive intelligence for a multinational energy company showed CrowdFS producing better results than an automatic state of the art technique.