scholarly journals An One Class Classification Approach to Non-relevance Feedback Document Retrieval

2005 ◽  
pp. 1216-1225
Author(s):  
Takashi Onoda ◽  
Hiroshi Murata ◽  
Seiji Yamada
Keyword(s):  
Relevance Feedback ◽  
Document Retrieval ◽  
Classification Approach ◽  
One Class Classification

Does relevance feedback improve document retrieval performance?

1978 ◽  
Vol 13 (1) ◽  
pp. 151-170 ◽  
Author(s):  
Robert E. Williamson
Keyword(s):  
Relevance Feedback ◽  
Document Retrieval ◽  
Retrieval Performance

Document ranking refinement using a Markov random field model

2012 ◽  
Vol 18 (2) ◽  
pp. 155-185 ◽  
Author(s):  
ESAÚ VILLATORO ◽  
ANTONIO JUÁREZ ◽  
MANUEL MONTES ◽  
LUIS VILLASEÑOR ◽  
L. ENRIQUE SUCAR
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Relevance Feedback ◽  
Document Retrieval ◽  
Document Similarity ◽  
Feedback Information ◽  
Markov Random ◽  
Novel Method ◽  
Cross Language ◽  
Recent Evaluation

AbstractThis paper introduces a novel ranking refinement approach based on relevance feedback for the task of document retrieval. We focus on the problem of ranking refinement since recent evaluation results from Information Retrieval (IR) systems indicate that current methods are effective retrieving most of the relevant documents for different sets of queries, but they have severe difficulties to generate a pertinent ranking of them. Motivated by these results, we propose a novel method to re-rank the list of documents returned by an IR system. The proposed method is based on a Markov Random Field (MRF) model that classifies the retrieved documents as relevant or irrelevant. The proposed MRF combines: (i) information provided by the base IR system, (ii) similarities among documents in the retrieved list, and (iii) relevance feedback information. Thus, the problem of ranking refinement is reduced to that of minimising an energy function that represents a trade-off between document relevance and inter-document similarity. Experiments were conducted using resources from four different tasks of the Cross Language Evaluation Forum (CLEF) forum as well as from one task of the Text Retrieval Conference (TREC) forum. The obtained results show the feasibility of the method for re-ranking documents in IR and also depict an improvement in mean average precision compared to a state of the art retrieval machine.

Mapping raised bogs with an iterative one-class classification approach

2016 ◽  
Vol 120 ◽  
pp. 53-64 ◽  
Author(s):  
Benjamin Mack ◽  
Ribana Roscher ◽  
Stefanie Stenzel ◽  
Hannes Feilhauer ◽  
Sebastian Schmidtlein ◽  
...  
Keyword(s):  
Classification Approach ◽  
Raised Bogs ◽  
One Class Classification
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