Abstract

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed (i.i.d.). At the same time, it offers the unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this work, we propose a method for learning to rank from network data when data distribution may change over time. The learned models can be used to predict the ranking of nodes in the network for new time periods. The proposed method modifies the SVMRank algorithm in order to emphasize the importance of models learned in time periods during which data follow a data distribution that is similar to that observed in the new time period. We evaluate our approach on several real world problems of learning to rank from network data, coming from the area of sensor networks.

Autore Pugliese

• Malerba Donato , Ceci Michelangelo

Tutti gli autori

• MALERBA D.;CECI M.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

0302-9743

ISBN

978-364231536-7

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

2

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile