Genetic algorithms in relevance feedback: a second test and new contributions

López-Pujalte, Cristina; Guerrero‐Bote, Vicente P.; Anegón, Félix de Moya

doi:10.1016/s0306-4573(02)00044-4

Cited by 34 publications

(21 citation statements)

References 27 publications

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“…Others who have used this measure include Fan et al (2006) and Horng & Yeh (2000). Lopez-Pujalte et al (2002) and Lopez-Pujalte et al (2003b) provide an extensive survey on 12 fitness functions, and found that the choice of fitness function was essential when guiding GA's through the search space. Other examples include use of the Guttman model, a statistical measure of rank correlation which has many of the same properties of average precision according to Tamine et al (2003).…”

Section: Fitness Functionsmentioning

confidence: 99%

Local search: A guide for the information retrieval practitioner

MacFarlane

Tuson

2009

Information Processing & Management

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThere are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in Information Retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a Query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The Query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR.

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Section: Fitness Functionsmentioning

confidence: 99%

Local search: A guide for the information retrieval practitioner

MacFarlane

Tuson

2009

Information Processing & Management

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“…In regard to search methods, most research studies have employed evolutionary algorithms (EAs). The EA flexibility enables the modeling of rank learning in many ways, such as through ranking function discovery [5,13,14], weight and parameter learning [15][16][17][18][19], among others. Independent to the model representation, a proper evaluation function is very important for the effectiveness and efficiency of EAs.…”

Section: Introductionmentioning

confidence: 99%

“…Although REFs have been shown to have applied a major rule to rank learning almost a decade ago [13,[15][16][17], in recent studies, little attention has been given to the design http://www.journal-bcs.com/content/20/1/7 and selection of more appropriate ones. Researchers have chosen popular REFs and applied them to new contexts and models without any theoretical or empirical evidence about its suitableness.…”

Section: Introductionmentioning

confidence: 99%

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Findings on ranking evaluation functions for feature weighting in image retrieval

et al. 2014

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Background: There are substantial benefits to be gained from ranking optimization in several information retrieval and recommendation systems. However, the analysis of ranking evaluation functions (REFs), which play a major role in many ranking optimization models, needs to be further investigated. An analysis of previous studies that investigated REFs was performed, and evidence was found which indicated that the choice of a proper REF is context sensitive. Methods:In this study, we analyze a broad set of REFs for feature weighting aimed at increasing the image retrieval effectiveness. The REFs analyzed sums ten and includes the most successful and representative REFs from the literature. The REFs were embedded into a genetic algorithm (GA)-based relevance feedback (RF) model, called WLSP-C±, aimed at improving image retrieval results through the use of learning weights for image descriptors and image regions. Results: Analyses of precision-recall curves in five real-world image data sets showed that one non-parameterized REF named F5, not analyzed in previous studies, overcame recommended ones, which require parameter adjustment. We also provided a computational analysis of the GA-based RF model investigated, and it was shown that it is linear in regard to the image data set cardinality. Conclusions:We conclude that REF F5 should be investigated in other contexts and problem scenarios centered on ranking optimization, as ranking optimization techniques rely heavily on the ranking quality measure.

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“…Getting the exact answer is a fundamental requirement for the traditional applications of DBMS, based mainly on numbers and small character strings. However, new DBMS are being increasingly required to support more complex data types such as images, videos, audio, time series and DNA sequences, among others [1]. This type of data management systems approach has given rise to Multimedia Digital Databases (MDD).…”

Section: Introductionmentioning

confidence: 99%

Query quality refinement in singular value decomposition to improve genetic algorithms for multimedia data retrieval

et al. 2011

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With the development of internet and availability of multimedia data capturing devices, the size of Multimedia Digital Database (MDD) collection is increasing rapidly. The complex data presented by such systems do not have the total ordering property presented by the traditional data handled by Database Management Systems (DBMSs). The quality of the search experience in such systems is also normally a big challenge since users from various domains require efficient data searching, browsing and retrieval tools. This has triggered an important research topic in Multimedia information retrieval concerning efficient and effective image similarity search. Modern search algorithms are fast and effective on a wide range of problems, but on MDD with a large number of parameters and observations, manipulations of large matrices, storage and retrieval of large amounts of information may render an otherwise useful method slow or inoperable. The focus of this work is the application of image enhancement technique, using histogram equalization, to the images retrieved using singular value decomposition (SVD). SVD is a linear algebra technique used for discovering correlations within data. The approach, herein referred to as query quality refinement (QQR) technique, improves the image similarity search result, and when incorporated with genetic algorithms further optimizes the search. These beneficial applications can be extended to other different types of multimedia data in various areas such as the P2P and WiMAX networks.

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Genetic algorithms in relevance feedback: a second test and new contributions

Cited by 34 publications

References 27 publications

Local search: A guide for the information retrieval practitioner

Local search: A guide for the information retrieval practitioner

Findings on ranking evaluation functions for feature weighting in image retrieval

Query quality refinement in singular value decomposition to improve genetic algorithms for multimedia data retrieval

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