NDLI: Application of the preference learning model to a human resources selection task

Content Provider	IEEE Xplore Digital Library
Author	Aiolli, F. De Filippo, M. Sperduti, A.
Copyright Year	2009
Description	Author affiliation: Department of Pure and Applied Mathematics, Padua University, Italy (Aiolli, F.; De Filippo, M.; Sperduti, A.)
Abstract	In many applicative settings there is the interest in ranking a list of items arriving from a data stream. In a human resource application, for example, to help selecting people for a given job role, the person in charge of the selection may want to get a list of candidates sorted according to their profiles and how much they are suited for the target job role. Historical data about past decisions can be analyzed to try to discover rules to help in defining such ranking. Moreover, samples have a temporal dynamics. To exploit this possibly useful information, here we propose a method that incrementally builds a committee of classifiers (experts), each one trained on the newer chunks of samples. The prediction of the committee is obtained as a combination of the rankings proposed by the experts which are “closer” to the data to rank. The experts of the committee are generated using the Preference Learning Model, a recent method which can directly exploit supervision in the form of preferences (partial orders between instances) and thus particularly suitable for rankings. We test our approach on a large dataset coming from many years of human resource selections in a bank.
Starting Page	203
Ending Page	210
File Size	3582089
Page Count	8
File Format	PDF
ISBN	9781424427659
DOI	10.1109/CIDM.2009.4938650
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Companies Machine learning Data warehouses Information retrieval Mathematics Information management Data mining Human resource management Testing
Content Type	Text
Resource Type	Article

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