A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing

Chen, Hsinchun; Shankaranarayanan, Ganesan; She, Linlin; Iyer, Anand

doi:10.1002/(sici)1097-4571(199806)49:8<693::aid-asi4>3.3.co;2-f

Cited by 35 publications

(33 citation statements)

References 1 publication

(3 reference statements)

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“…ChenÕs group (Chen, 1995;Chen & Iyer, 1998) use a GA to learn the terms of a query that best represent a user-supplied set of documents (they call this process ''inductive query by example''). They follow with an intelligent agent that uses this algorithm to implement the feedback module.…”

Section: Antecedentsmentioning

confidence: 99%

“…In recent years, there have appeared numerous applications of ''genetic algorithms'' (GAs) to information retrieval (Belew, 1989;Chen, 1995;Chen, Chung, & Ramsey, 1998;Chen & Iyer, 1998;Cordo on, Moya, & Zarco, 2000Gordon, 1988aGordon, ,b, 1991Horng & Yeh, 2000;Kraft, Petry, Buckles, & Sadasivan, 1994Lo opez-Pujalte, 2000;Lo opez-Pujalte, Guerrero Bote, & Moya Anego on, 2002a,b;Martı ın-Bautista, 2000;Martı ın-Bautista, Vila, & Larsen, 1999;Raghavan & Agarwal, 1987;Raghavan & Birchard, 1979;Robertson & Willett, 1994, 1996Sanchez, 1994;Sanchez, Miyano, & Brachet, 1995;Sanchez & Pierre, 1994;Smith & Smith, 1997;Vrajitoru, 1997Vrajitoru, , 1998Yang & Korfhage, 1992, 1993, 1994. Most of these applications refer to ''relevance feedback'', as is indicated in a review of the topic (Cordo on, Moya, & Zarco, 1999).…”

Section: Introductionmentioning

confidence: 99%

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

López-Pujalte

Guerrero‐Bote

Anegón

2003

Information Processing & Management

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The present work is the continuation of an earlier study which reviewed the literature on relevance feedback genetic techniques that follow the vector space model (the model that is most commonly used in this type of application), and implemented them so that they could be compared with each other as well as with one of the best traditional methods of relevance feedback--the Ide dec-hi method. We here carry out the comparisons on more test collections (Cranfield, CISI, Medline, and NPL), using the residual collection method for their evaluation as is recommended in this type of technique. We also add some fitness functions of our own design.

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Section: Antecedentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

López-Pujalte

Guerrero‐Bote

Anegón

2003

Information Processing & Management

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“…In systems based on relevance feedback [26] the user gives feedback on the relevance of documents retrieved by his original query. IQBE [6] is a process in which the user does not provide a query, but document examples and the algorithms induce the key concepts in order to find other relevant documents. Mainly two information retrieval problems have been tackled with GAs: assigning weights to the query terms [21,28,23,14,13], and selecting query terms.…”

Section: Modelmentioning

confidence: 99%

Structure of morphologically expanded queries: A genetic algorithm approach

Araujo

Zaragoza

Pérez-Agüera

et al. 2010

Data & Knowledge Engineering

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a b s t r a c tIn this paper we deal with two issues. First, we discuss the negative effects of term correlation in query expansion algorithms, and we propose a novel and simple method (query clauses) to represent expanded queries which may alleviate some of these negative effects. Second, we discuss a method to optimize local query-expansion methods using genetic algorithms, and we apply this method to improve stemming. We evaluate this method with the novel query representation method and show very significant improvements for the problem of stemming optimization.

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“…In Neural Networks, associations between terms or between terms/documents can be established with the links assigned some weight (a floating-point number). Methods such as back propagation can then be investigated over the networks created by these associations (Chen Shankaranarayanan and She, 1998).…”

Section: The Role Of the Fit Between Technique And Taskmentioning

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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A machine learning approach to inductive query by examples: An experiment using relevance feedback, ID3, genetic algorithms, and simulated annealing

Cited by 35 publications

References 1 publication

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

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

Structure of morphologically expanded queries: A genetic algorithm approach

Local search: A guide for the information retrieval practitioner

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