A two-stage hybrid ant colony optimization for high-dimensional feature selection

Ma, Wenping; Zhou, Xiaobo; Zhu, Hao; Li, Longwei; Jiao, Licheng

doi:10.1016/j.patcog.2021.107933

Cited by 102 publications

(36 citation statements)

References 34 publications

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“…Gan et al [13] used ACO to propagate path optimization of product attribute design changes. Ma et al [24] applied ACO for high-dimensional feature selection.…”

Section: The Ant Colony Optimization Metaheuristicmentioning

confidence: 99%

Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy

Oliveira

Silva

2021

RAIRO-Oper. Res.

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This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.

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“…Gan et al [13] used ACO to propagate path optimization of product attribute design changes. Ma et al [24] applied ACO for high-dimensional feature selection.…”

Section: The Ant Colony Optimization Metaheuristicmentioning

confidence: 99%

Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy

Oliveira

Silva

2021

RAIRO-Oper. Res.

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“…ACO algorithm is also widely used in feature selection. Zhou et al [ 10 ] proposed a two-stage hybrid ACO for high-dimensional feature se-lection (TSHFS-ACO), which uses the interval strategy to determine the size of OFS for the following OFS search and helps to reduce the complexity of the algorithm and alleviate the algorithm from getting into a local optimum. In solving constraint satisfaction problem (CSP), in order to overcome the shortcomings of low solution quality and slow convergence speed based on ACO algorithm, Guan et al [ 11 ] proposed an improved ant colony optimization with an automatic updating mechanism (AU-ACO).…”

Section: Introductionmentioning

confidence: 99%

An improved ant colony optimization algorithm based on context for tourism route planning

Liang

Jiao

et al. 2021

PLoS ONE

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To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

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“…Feature selection is an important process in the analysis of high-dimensional data [5,6]. Rough set theory is a mathematical tool that deals with imprecise, inconsistent, and incomplete problems [7].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Classification Method for High‐Dimensional Data Using Neighborhood Rough Set

Zhang

Guang

et al. 2021

Complexity

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Mining useful knowledge from high-dimensional data is a hot research topic. Efficient and effective sample classification and feature selection are challenging tasks due to high dimensionality and small sample size of microarray data. Feature selection is necessary in the process of constructing the model to reduce time and space consumption. Therefore, a feature selection model based on prior knowledge and rough set is proposed. Pathway knowledge is used to select feature subsets, and rough set based on intersection neighborhood is then used to select important feature in each subset, since it can select features without redundancy and deals with numerical features directly. In order to improve the diversity among base classifiers and the efficiency of classification, it is necessary to select part of base classifiers. Classifiers are grouped into several clusters by k-means clustering using the proposed combination distance of Kappa-based diversity and accuracy. The base classifier with the best classification performance in each cluster will be selected to generate the final ensemble model. Experimental results on three Arabidopsis thaliana stress response datasets showed that the proposed method achieved better classification performance than existing ensemble models.

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A two-stage hybrid ant colony optimization for high-dimensional feature selection

Cited by 102 publications

References 34 publications

Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy

Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy

An improved ant colony optimization algorithm based on context for tourism route planning

An Ensemble Classification Method for High‐Dimensional Data Using Neighborhood Rough Set

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