Credit scoring profiles the client relationships of empirical attributes (variables) and leverages a scoring model to draw client's credibility. However, empirical attributes often contains a certain degree of uncertainty and requires feature selection. Bayesian network (BN) is an important tool for dealing with uncertain problems and information. Mutual information (MI) measures dependencies between random variables and is therefore a suitable feature selection technique for evaluating the relationship between variables in a complex classification tasks. Using Bayesian network as a statistical model, this study leverages mutual information to build a credit scoring model called BNMI. The learned Bayesian network structure is adaptively adjusted according to mutual information. Empirical study compared the results of BNMI with three existing baseline models. The results show that the proposed model outperforms the baseline models in terms of receiver operating characteristic (ROC), indicating promising application of our BNMI in the credit scoring area.
Abstract. Most state-of-the-art studies either conduct peer assessment or adopt bibliometric indicators for institution evaluation. However, peer assessments are labor intensive and time consuming, and existing bibliometric methods may produce a biased evaluation result because they do not synthetically model many crucial factors that reflect the academic performance of institutions in a unified way. Thus,we propose a factor graph-based institution ranking model to leverage institutions' individual information (i.e., quantitative and qualitative information) and scholarly network information (i.e., collaborative intensity) in this paper. We choose the peer assessment result from the best-known U.S. News & World Report as the ground truth and conduct a case study on the U.S. institution ranking in the library and information science (LIS) research field. The experimental results indicate that our approach can be a better alternative for the manual peer assessment for institution evaluation when compared with existing bibliometrics methods.
The W3C's Media Fragments URI 1.0 specification provides for a media-format independent, standard means of addressing media fragments on the Web using Uniform Resource Identifiers (URIs). Thus, a key requirement is for the User Agent (UA) to efficiently retrieve media fragments identified by URIs from the regular media server over the HTTP protocol. This paper addresses the issue of how to construct a Media Fragment URI aware UA. We propose an approach for achieving such a UA, focusing on fully indexable container formats. Our approach consists of a set of algorithms capable of performing URI-based media fragment retrieval. Algorithm implementation and experimental results show that our approach is achievable and is able to greatly reduce time and bandwidth costs compared to the traditional approach of downloading the entire media resource.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.