Order‐based fitness functions for genetic algorithms applied to relevance feedback

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

doi:10.1002/asi.10179

Cited by 49 publications

(39 citation statements)

References 32 publications

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Order By: Relevance

“…It has been suggested by Fan et al (2004a) from Lopez-Pujalte et al (2003a) that order based functions yield better results than set based methods, and this is not inconsistent with evidence found in Okapi experiments. The most popular fitness function appears to be average precision; a recall-based precision measure used in standard IR evaluations such as TREC.…”

Section: Fitness Functionssupporting

confidence: 54%

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 Functionssupporting

confidence: 54%

Local search: A guide for the information retrieval practitioner

MacFarlane

Tuson

2009

Information Processing & Management

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“…The fitness value for the features subset is comparative with its gained information. Lopez introduced a fitness function which constructed as follow (López-Pujalte et al, 2003): firstly, using the scalar products calculate the similarity index of the query vectors, then, sort the results in a decreasing order of similarity. Finally, calculates the fitness value (F) of the chromosome using Equation (1).…”

Section: Ids Feature Selection Algorithm Usingmentioning

confidence: 99%

Hybrid Feature Selection Algorithm for Intrusion Detection System

Hasani

Othman

Kahaki

2014

Journal of Computer Science

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Network security is a serious global concern. Usefulness Intrusion Detection Systems (IDS) are increasing incredibly in Information Security research using Soft computing techniques. In the previous researches having irrelevant and redundant features are recognized causes of increasing the processing speed of evaluating the known intrusive patterns. In addition, an efficient feature selection method eliminates dimension of data and reduce redundancy and ambiguity caused by none important attributes. Therefore, feature selection methods are well-known methods to overcome this problem. There are various approaches being utilized in intrusion detections, they are able to perform their method and relatively they are achieved with some improvements. This work is based on the enhancement of the highest Detection Rate (DR) algorithm which is Linear Genetic Programming (LGP) reducing the False Alarm Rate (FAR) incorporates with Bees Algorithm. Finally, Support Vector Machine (SVM) is one of the best candidate solutions to settle IDSs problems. In this study four sample dataset containing 4000 random records are excluded randomly from this dataset for training and testing purposes. Experimental results show that the LGP_BA method improves the accuracy and efficiency compared with the previous related research and the feature subcategory offered by LGP_BA gives a superior representation of data.

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“…They profoundly expressed applying the evolutionary algorithms for association rules to improve prediction accuracy as their future work. C. LopezPujalte, V. P. G. Bote, and F. de Moya Anegon [3] evaluated the efficacy of genetic algorithm for various order based fitness functions that are designed as measures of goodness of the solutions. K. C. Wiese and E. Glen [12] presented a genetic algorithm to predict the secondary structure of RNA molecule where secondary structure is encoded as a permutation.…”

Section: Related Workmentioning

confidence: 99%