A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

He, Chunlin; Zhang, Yong; Gong, Dunwei

doi:10.3390/rs12203456

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…One of the methods employed for feature selection is to search for a minimal subset (using meta-heuristic algorithms) within a full set of features that has the same level of discernibility as that of the full feature set. Many recent results for the feature selection methods using meta-heuristic algorithms are available in the literature [30][31][32][33][34][35][36]. This type of search for a solution can be achieved by the application of rough set theory, which was proposed in [37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

Jaddi

Abdullah

2021

IEEE Access

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The selection of features from data, as one of the most important tasks in data mining, strongly affects the accuracy of classification. The removal of irrelevant and redundant features from data while simultaneously avoiding information loss is the main objective of feature selection. Feature selection is possible using rough set theory and meta-heuristic algorithms. In this paper, a novel auctionbased optimization algorithm (ABOA) is proposed to contribute to generating an effective algorithm with a good trade-off between exploration and exploitation. This new algorithm simulates the auction sale process, where bidders offer higher/lower amounts to outbid each other. Auctions are categorized into ascending auctions and descending auctions and thus respectively represent maximization and minimization problems in ABOA. In the first step of the ABOA after initialization, a predefined number of bidders is selected and an auction is performed between them. The winner is selected and another auction is performed between the winner and a predefined number of the winner's neighbors. The winner of this round of auction is added to the winner list. This process is iterated until a predefined number of winners is found. Finally, one more auction is performed between all the winners on the winner list and the winner of that auction becomes the final winner. The algorithm with different parameter setting scenarios is tested on 25 benchmark test functions. The algorithm with the best results is then used to perform feature selection on 18 UCI datasets. The feature selection and classification accuracy results are compared with state-of-the-art results. The statistical analysis of the results proves the ability of the algorithm to solve optimization problems.

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Section: Introductionmentioning

confidence: 99%

A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

Jaddi

Abdullah

2021

IEEE Access

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“…Mathematical Problems in Engineering [24] proposed an improved multiobjective ABC algorithm to solve multistage resource levelling problems. Furthermore, He et al [25] proposed a pseudolabel guided ABC algorithm to eliminate the redundant and noise bands from the hyperspectral images. Wang et al [26] used a multiobjective feature selection algorithm based on the reduction of samples and ABC algorithm to reduce computation cost.…”

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confidence: 99%

“…e example calculation is given in Section 5.1. (c) Identification of search space by employed bees Equation(25) gives the value of the addition of critical dimensions of the assembly. Based on the increasing order of these values, the bees are listed.…”

mentioning

confidence: 99%

Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm

Sivakumar

Nagarajan

Kandasamy

et al. 2021

Mathematical Problems in Engineering

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Selective assembly is a method where components made with wider tolerance are grouped into a number of bins. Based on the best combination of the bin, the corresponding group components are randomly selected and matched together to make an assembly. Existing techniques focused on equal group number partitioning of components, equal probability, equal group width, and equal area methods to minimize either clearance variation or surplus parts using different optimization techniques. Mostly, simple assemblies with two or three components are worked by various authors in the literature without considering their original dimension by considering only their component’s tolerance. In the present work, components are classified into different unequal group numbers based on their tolerance values. The interrelated dimensional assemblies are made in a single stage by matching the parts based on the best bin combination obtained by the artificial bee colony algorithm. A simple linear assembly and a three-armed knuckle joint assembly are considered examples of problems to demonstrate the effectiveness of the proposed method by minimizing the manufacturing cost.

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Evolutionary Machine Learning in Environmental Science

Batista,

Silva

2023

Genetic and Evolutionary Computation

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A Pseudo-Label Guided Artificial Bee Colony Algorithm for Hyperspectral Band Selection

Cited by 12 publications

References 49 publications

A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

A Novel Auction-Based Optimization Algorithm and Its Application in Rough Set Feature Selection

Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm

Evolutionary Machine Learning in Environmental Science

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