Determination of sublevel stoping layout using a network flow algorithm and the MRMR classification system

Esmaeili, Ahmadreza; Hamidi, Jafar Khademi; Mousavi, Amin

doi:10.1016/j.resourpol.2022.103265

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…To assess the impact on each class, some feature selection methods in HSI datasets are compared in the experiment: maximum information minimum redundancy (MRMR) [47], joint mutual information with class correlation (JOMIC) [48], joint mutual information maximization (JMIM) [49], conditional mutual information maximization (CMIM) According to Table 4, the proposed method exhibits significantly higher reductive efficiency than other EA-based feature selection methods. Specifically, it selects less than 20% of the features from the HSI dataset, resulting in the selection of only 42 features out of a total of 176 bands in the KSC dataset while achieving a prominent OA.…”

Section: Comparison With Other Feature Selection Methodsmentioning

confidence: 99%

Section: Comparison With Other Feature Selection Methodsmentioning

confidence: 99%

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Discarding–Recovering and Co-Evolution Mechanisms Based Evolutionary Algorithm for Hyperspectral Feature Selection

Liao,

Li,

Liu

et al. 2023

Remote Sensing

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With the improvement of spectral resolution, the redundant information in the hyperspectral imaging (HSI) datasets brings computational, analytical, and storage complexities. Feature selection is a combinatorial optimization problem, which selects a subset of feasible features to reduce the dimensionality of data and decrease the noise information. In recent years, the evolutionary algorithm (EA) has been widely used in feature selection, but the diversity of agents is lacking in the population, which leads to premature convergence. In this paper, a feature selection method based on discarding–recovering and co-evolution mechanisms is proposed with the aim of obtaining an effective feature combination in HSI datasets. The feature discarding mechanism is introduced to remove redundant information by roughly filtering the feature space. To further enhance the agents’ diversity, the reliable information interaction is also designed into the co-evolution mechanism, and if detects the event of stagnation, a subset of discarded features will be recovered using adaptive weights. Experimental results demonstrate that the proposed method performs well on three public datasets, achieving an overall accuracy of 92.07%, 92.36%, and 98.01%, respectively, and obtaining the number of selected features between 15% and 25% of the total.

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Section: Comparison With Other Feature Selection Methodsmentioning

confidence: 99%