The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jan Vanthienen - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of social network classifiers for predicting churn in the Telecommunication Industry
arXiv: Social and Information Networks, 2020Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in Telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the Telecommunication Industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
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Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
Expert Systems with Applications, 2017Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Social network analytics methods are being used in the Telecommunication Industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from Telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the Telecommunication Industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the Telecommunication Industry in an optimal way, ranging from network architecture to model building and evaluation.
María Óskarsdóttir - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of social network classifiers for predicting churn in the Telecommunication Industry
arXiv: Social and Information Networks, 2020Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in Telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the Telecommunication Industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
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Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
Expert Systems with Applications, 2017Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Social network analytics methods are being used in the Telecommunication Industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from Telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the Telecommunication Industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the Telecommunication Industry in an optimal way, ranging from network architecture to model building and evaluation.
Francesco Galati - One of the best experts on this subject based on the ideXlab platform.
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the adoption of open innovation within the Telecommunication Industry
European Journal of Innovation Management, 2012Co-Authors: Barbara Bigliardi, Alberto Ivo Dormio, Francesco GalatiAbstract:Purpose – The paper, covering the actual argument of open innovation, aims to answer two main research questions, namely: “Which open innovation approach is adopted by the companies belonging to the ICTs Industry?” and “Which types of collaborations are carried out by the companies and which are the dynamics that characterize it?”. Design/methodology/approach – In order to answer the research questions a multiple case study methodology is adopted. The research framework was structured in three main phases: first, a literature review on the matter of open innovation in general and within the ICTs Industry in particular, as well as of the specific features of the Industry investigated, was carried out. Second, a list of questions containing the main issues that arose from the previous step has been designed for the case study protocol, to be used in the following structured interviews. Finally, structured direct interviews were conducted on three important Italian companies active in the Telecommunications area. Findings – Results highlighted different ways to manage the open innovation processes, based on teamwork or task forces, and the different roles, more or less proactive, that an information communication technology (ICT) company may undertake within this process. Moreover, they show that ICT companies acquire external knowledge and skills mainly from universities and research centers, as well as from value chain’s actors (suppliers in primis). Originality/value – Still little attention has been paid to the understanding of the open innovation approach of Italian firms belonging to the ICT Industry, thus the authors believe that this paper may represent a valuable basis for future research on the open innovation issues in the field of ICT.
Bart Baesens - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of social network classifiers for predicting churn in the Telecommunication Industry
arXiv: Social and Information Networks, 2020Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in Telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the Telecommunication Industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
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Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
Expert Systems with Applications, 2017Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Social network analytics methods are being used in the Telecommunication Industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from Telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the Telecommunication Industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the Telecommunication Industry in an optimal way, ranging from network architecture to model building and evaluation.
Wouter Verbeke - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of social network classifiers for predicting churn in the Telecommunication Industry
arXiv: Social and Information Networks, 2020Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in Telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the Telecommunication Industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
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Social network analytics for churn prediction in telco: Model building, evaluation and network architecture
Expert Systems with Applications, 2017Co-Authors: María Óskarsdóttir, Cristián Bravo, Wouter Verbeke, Carlos Sarraute, Bart Baesens, Jan VanthienenAbstract:Social network analytics methods are being used in the Telecommunication Industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from Telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the Telecommunication Industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the Telecommunication Industry in an optimal way, ranging from network architecture to model building and evaluation.
