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...

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Bibliographic Details
Published in:Procedia computer science 2017, Vol.112, p.822-832
Main Authors: Pintas, Julliano Trindade, Correia, Luís, Bicharra Garcia, Ana Cristina
Format: Article
Language:English
Subjects:
business intelligence
collective intelligence
crowd
dimensionality reduction
document retrieval
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2017.08.074