A Review of Wrapper Feature Selection in Content Based Image Retrieval Systems

Lotfabadi, Maryam Shahabi; Zhan, Yongzhao; Tabrizi, Amir Bashirzadeh

doi:10.1145/3195106.3195113

Cited by 2 publications

(1 citation statement)

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“…Application in bioinformatics [41], content based image retrieval [42], and text mining [43] among other datasets that are high dimensional in nature pose challenges for metaheuristic-based wrappers. However, it is possible that clever hybrid proposals could be successful, due to the complex internal structure of these datasets [11], [15].…”

Section: Related Work a Wrappers For Feature Selectionmentioning

confidence: 99%

Wrapper for Building Classification Models Using Covering Arrays

et al. 2019

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Wrapper methods are a type of feature selection method that finds a subset of variables to improve the performance of a classifier by removing redundant and irrelevant variables. The use of a wrapper implies that each time a candidate solution is explored, the classifier is evaluated on the quality measures selected (e.g. accuracy or precision). Though robust, this iteration across several candidate solutions can become computationally intensive and time-consuming. In this paper we propose a wrapper, that is based on binary Covering Arrays (CAs), and binary Incremental Covering Arrays (ICAs), that have been widely used for experimental design and fault detection in software and hardware testing. The new wrapper was evaluated with six classifiers on seven data sets. The results show that the CAs and ICAs with strength 6 significantly improve the performance and reduces the number of variables required by the classifier. A comparative analysis of the proposed method against wrappers based on other search approaches such as genetic algorithms (GA) and particle swarm optimization (PSO), shows that the proposed method yields results similar to GA, but not to PSO, with differences to PSO, in accuracy, which in the majority of cases is below 0.04. This lack of accuracy, by which the new wrapper fails to match PSO, is offset by the fact that the user does not need to fine tune algorithm parameters, such as velocity ranges, timing, cognitive coefficient, and social coefficient, while it is also much easier to program in parallel.INDEX TERMS Classification algorithms, covering arrays, random forest, support vector machines, genetic algorithms, particle swarm optimization. HUGO DORADO received the B.Sc. degree in statistics from the Universidad del Valle, Cali, Colombia, in 2013, and the M.Sc. degree in computing from the Universidad del Cauca, Popayán, Colombia, in 2019.He is currently a Research Assistant with the Research Group of Decision and Policy Analysis (DAPA), International Center for Tropical Agriculture (CIAT). His role has been focused in analyzing observational information in agriculture by using analytical methodologies for detecting patterns associated with either high or low yields. His research interests include data mining, computational intelligence, and statistical science.

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