A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Yongliang, Chen; Wu, Wei; Zhao, Qing

doi:10.3390/min9050317

Cited by 28 publications

(10 citation statements)

References 24 publications

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“…A valuable contribution by Chen et al [9] provides a novel method for the use of a one-class support vector machine (OCSVM) algorithm by combining it with the bat algorithm. This combination results in the automatic optimization of the initialization parameters of the OCSVM.…”

Section: Methodsmentioning

confidence: 99%

Editorial for Special Issue “Novel Methods and Applications for Mineral Exploration”

Alexandre

2020

Minerals

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Section: Methodsmentioning

confidence: 99%

Editorial for Special Issue “Novel Methods and Applications for Mineral Exploration”

Alexandre

2020

Minerals

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“…In each iteration, (0 < t < T), the global search is conducted to update the flight speed and space position of each bat. The space positions of each bat are used to calculate the fitness value of the objective function, and the one corresponding to the maximum fitness value is selected as the current optimal position [52][53][54][55]. The updating formula of speed and space position is as follows:…”

Section: Bat Algorithmmentioning

confidence: 99%

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

et al. 2021

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Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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“…In other word, condition of normal data distribution is not required in non-parametric classification approaches. Decision trees, expert systems, Support Vector Machine (SVM), deep learning and neural networks are typical examples of non-parametric classifiers which are widely preferred for classification remote sensing images from complex terrain [2][3][4][5][6][7]. Currently SVM, deep learning and neural networks-based algorithms are most widely being used because of its nonparametric technique and capability to distinguish complicated decision boundaries [8].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, bagging, boosting, or a hybrid of both techniques are being increasingly used to enhance the classification performance of non-parametric as well as parametric classifiers [6]. These methods have also been utilized in the framework of decision trees [10] and SVM [2,4,7,[11][12][13] to enhance classifications accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

2020

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Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

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A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Cited by 28 publications

References 24 publications

Editorial for Special Issue “Novel Methods and Applications for Mineral Exploration”

Editorial for Special Issue “Novel Methods and Applications for Mineral Exploration”

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

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