WD2O: a novel wind driven dynamic optimization approach with effective change detection

Boulesnane, Abdennour; Meshoul, Souham

doi:10.1007/s10489-017-0895-2

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…Therefore, each optimization problem requires a thorough study that allows to find the best algorithm. Recently, a new dynamic optimization algorithm has been proposed in [6], called "Wind Driven Dynamic Optimization Algorithm (WD2O)". The characteristic property of this metaheuristic is the classification, it suggests to set regions of the search space as promising and non-promising regions with accordance to low and high pressure regions in the natural model.…”

Section: Wind Driven Dynamic Optimization Algorithm (Wd2o)mentioning

confidence: 99%

“…In this context and as a first initiative of this kind, we present in this paper a new approach that deals with the problem of streaming feature selection by introducing dynamic optimization during the selection of the best attributes. Motivated by the fact that the problem of online feature selection is a dynamic problem whose dimension (feature) changes over time, we propose a hybridization between the WD2O dynamic optimization algorithm proposed in [6] and the Online Streaming Feature Selection algorithm (OSFS) [2], whose objective is to find a subset of optimal attributes to ensure better classification of unclassified data. Therefore, the main contribution of this work consists of a new hybrid approach called Dynamic Online Streaming Feature Selection (DOSFS) that exhibits the following features:…”

Section: Introductionmentioning

confidence: 99%

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Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization

Boulesnane

Meshoul

2018

Computational Intelligence and Its Applications

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Feature selection is a key issue in machine learning and data mining. A great deal of effort has been devoted to static feature selection. However, with the assumption that features occur over time, methods developed so far are difficult to use if not applicable. Therefore, there is a need to design new methods to deal with streaming feature selection (SFS). In this paper, we propose the use of dynamic optimization to handle the dynamic nature of SFS with the ultimate goal to improve the quality of the evolving subset of selected features. A hybrid model is developed to fish out relevant features set as unnecessary by an online feature selection process. Experimental results show the effectiveness of the proposed framework compared to some state of the art methods.

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Section: Wind Driven Dynamic Optimization Algorithm (Wd2o)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective Streaming Evolutionary Feature Selection Using Dynamic Optimization

Boulesnane

Meshoul

2018

Computational Intelligence and Its Applications

Self Cite

View full text Add to dashboard Cite

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Feature subset selection for data and feature streams: a review

2023

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Real-world problems are commonly characterized by a high feature dimensionality, which hinders the modelling and descriptive analysis of the data. However, some of these data may be irrelevant or redundant for the learning process. Different approaches can be used to reduce this information, improving not only the speed of building models but also their performance and interpretability. In this review, we focus on feature subset selection (FSS) techniques, which select a subset of the original feature set without making any transformation on the attributes. Traditional batch FSS algorithms may not be adequate to efficiently handle large volumes of data, either because memory problems arise or data are received in a sequential manner. Thus, this article aims to survey the state of the art of incremental FSS algorithms, which can perform more efficiently under these circumstances. Different strategies are described, such as incrementally updating feature weights, applying information theory or using rough set-based FSS, as well as multiple supervised and unsupervised learning tasks where the application of FSS is interesting.

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