Ranking Based

A new DEA ranking system based on changing the reference set [An article from: European Journal of Operational Research]
$7.95
This digital document is a journal article from European Journal of Operational Research, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This research proposes a new ranking system for extreme efficient DMUs (Decision Making Units) based upon the omission of these efficient DMUs from reference set of the inefficient DMUs. We state and prove some facts related to our model. A numerical example where the proposed method is compared with traditional ranking approaches is shown.

Variable screening and ranking using sampling-based sensitivity measures [An article from: Reliability Engineering and System Safety]
$7.95
This digital document is a journal article from Reliability Engineering and System Safety, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
This paper presents a methodology for screening insignificant random variables and ranking significant important random variables using sensitivity measures including two cumulative distribution function (CDF)-based and two mean-response based measures. The methodology features (1) using random samples to compute sensitivities and (2) using acceptance limits, derived from the test-of-hypothesis, to classify significant and insignificant random variables. Because no approximation is needed in either the form of the performance functions or the type of continuous distribution functions representing input variables, the sampling-based approach can handle highly nonlinear functions with non-normal variables. The main characteristics and effectiveness of the sampling-based sensitivity measures are investigated using both simple and complex examples. Because the number of samples needed does not depend on the number of variables, the methodology appears to be particularly suitable for problems with large, complex models that have large numbers of random variables but relatively few numbers of significant random variables.

Re-ranking method based on inter-document distances [An article from: Information Processing and Management]
$5.95
This digital document is a journal article from Information Processing and Management, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Lately there has been intensive research into the possibilities of using additional information about documents (such as hyperlinks) to improve retrieval effectiveness. It is called data fusion, based on the intuitive principle that different document and query representations or different methods lead to a better estimation of the documents' relevance scores. In this paper we propose a new method of document re-ranking that enables us to improve document scores using inter-document relationships. These relationships are expressed by distances and can be obtained from the text, hyperlinks or other information. The method formalizes the intuition that strongly related documents should not be assigned very different weights.

Concept-based ranking: a case study in the juridical domain [An article from: Information Processing and Management]
$5.95
This digital document is a journal article from Information Processing and Management, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
We explore the idea of automatically using the concepts of a thesaurus to improve search results. Our focus is on improving average precision figures, not recall. This is of interest because there is a tendency to accept the view that thesauri are recall-enhancing devices that are not good for improving precision figures. In our approach, the query terms are used to match concepts in the thesaurus. These concepts are then used to find other related concepts (narrow, broad, synonym), which are interpreted as independent sources of evidential knowledge. Each source of evidence is used to produce a separate concept-based ranking of the documents in the collection. These partial rankings are then combined into a final ranking. For this, we use a Bayesian belief network. To validate our ideas we do a case study in the juridical domain. Using a juridical thesaurus and a test collection containing more than 500 hundred thousand juridical documents, obtained from the legal court system in Brazil, we compare our concept-based ranking with the standard vectorial ranking. The results indicate improvements in average precision figures of roughly 30%.