A review on the application of evolutionary computation to information retrieval

Cordón, Óscar; Liu, Xinyue; López-Pujalte, Cristina; Luque, Marı́a José; Zarco, Carmen

doi:10.1016/j.ijar.2003.07.010

Cited by 70 publications

(42 citation statements)

References 32 publications

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“…It is called Boolean IQBE-GP, and it is able to derive Boolean queries. As it is usual in the field [14], this approach is guided by a weighted fitness function combining the classical retrieval accuracy criteria, precision and recall [16]. In [20], Kraft et al propose an IQBE technique to learn the whole composition of extended Boolean queries for Fuzzy IR systems.…”

Section: The Query Definition Problemmentioning

confidence: 99%

“…Evolutionary Algorithms (EAs) have been used for IR purposes [14], being the query definition problem one of the most studied one. Concretely, the use of Multiobjective EAs (MOEAs) in the query definition problem has been proved as advantageous, due to MOEAs can learn a set of queries with good precisionrecall trade-off in a unique run [26].…”

Section: Introductionmentioning

confidence: 99%

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A Multiobjective Evolutionary Algorithm for spam e-mail filtering

López-Herrera

Herrera

2008

2008 3rd International Conference on Intelligent System and Knowledge Engineering

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Section: The Query Definition Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiobjective Evolutionary Algorithm for spam e-mail filtering

López-Herrera

Herrera

2008

2008 3rd International Conference on Intelligent System and Knowledge Engineering

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“…Some Evolutionary Computation (EC) techniques have been used for evolving TWS or evolving term weights (Cummins, 2008;Cordan et al, 2003). However, such approaches have an important drawback as we discussed next.…”

Section: Introductionmentioning

confidence: 99%

“…But for user's queries that are different from those in the query set, only random effectiveness is achieved. In addition, TWS evolved with Genetic Programming (GP) as in (Cummins, 2008;Cordan et al, 2003) are based on the characteristics of the test collections and hence, not easily generalizable to be effective on collections with different characteristics. Moreover, these proposed EC techniques assume that document collections are static and not dynamic also.…”

Section: Introductionmentioning

confidence: 99%

Term frequency with average term occurrences for textual information retrieval

Ibrahim

Landa-Silva

2015

Soft Comput

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Ibrahim, O. and Landa-Silva, Dario (2016) Term frequency with average term occurrences for textual information retrieval. Soft Computing, 20 (8 A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Abstract In the context of Information Retrieval (IR) from text documents, the term-weighting scheme (TWS) is a key component of the matching mechanism when using the vector space model (VSM). In this paper we propose a new TWS that is based on computing the average term occurrences of terms in documents and it also uses a discriminative approach based on the document centroid vector to remove less significant weights from the documents. We call our approach Term Frequency With Average Term Occurrence (TF-ATO). An analysis of commonly used document collections shows that test collections are not fully judged as achieving that is expensive and may be infeasible for large collections. A document collection being fully judged means that every document in the collection acts as a relevant document to a specific query or a group of queries. The discriminative approach used in our proposed approach is a heuristic method for improving the IR effectiveness and performance, and it has the advantage of not requiring previous knowledge about relevance judgements. We compare the performance of the proposed TF-ATO to the well-known TF-IDF approach and show that using TF-ATO results in better effectiveness in both static and dynamic document collections. In addition, this paper investigates the impact that stop-words removal and our discriminative approach have on TF-IDF and TF-ATO. The results show that both, stopwords removal and the discriminative approach, have a positive effect on both term-weighting schemes. More importantly, it is shown that using the proposed discriminative approach is beneficial for improving IR effectiveness and performance with no information in the relevance judgement for the collection.

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“…Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks [29,102,136] and, at the same time, are severely affected by scaling up problems. The evolutionary approach has a major bottleneck in the evaluation of the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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A review on the application of evolutionary computation to information retrieval

Cited by 70 publications

References 32 publications

A Multiobjective Evolutionary Algorithm for spam e-mail filtering

A Multiobjective Evolutionary Algorithm for spam e-mail filtering

Term frequency with average term occurrences for textual information retrieval

Scaling up data mining algorithms: review and taxonomy

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