Using RankBoost to Compare Retrieval Systems
Huyen-Trang Vu and Patrick Gallinari
In: CIKM 2005, 31 Oct - 05 Nov 2005, Germany.
This paper presents a new pooling method for constructing the assessment
sets used in the evaluation of retrieval systems. Our proposal is based
on RankBoost, a machine learning voting algorithm. It leads to smaller pools than classical pooling
and thus reduces the manual assessment workload for building test collections.
Experimental results obtained on an XML document collection demonstrate the
effectiveness of the approach according to different evaluation criteria.