Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Zhou, Yi; Tian, Senlin; Chen, Jianping; Liu, Yao; Li, Chaozhu

doi:10.3390/s22051948

Cited by 7 publications

(5 citation statements)

References 54 publications

(59 reference statements)

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“…It can be seen that spectral features are of the highest importance, followed by SAR features, and finally terrain features. B11 (SWIR) obtained the highest raw importance value (18.5) because SWIR provides valuable information based on color, reflectance, and absorption properties for rock identification.44 The raw importance values of texture and polarization features in the vertical horizontal (VH) band are also relatively high, as VH band backscattering coefficients are easily distinguishable in mining research 45 . In addition, all types of features are considered important, indicating the effectiveness of the constructed feature combination.…”

Section: Discussionmentioning

confidence: 99%

“…44 The raw importance values of texture and polarization features in the vertical horizontal (VH) band are also relatively high, as VH band backscattering coefficients are easily distinguishable in mining research. 45 In addition, all types of features are considered important, indicating the effectiveness of the constructed feature combination.…”

Section: Discussionmentioning

confidence: 99%

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Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Chen,

2023

J. Appl. Rem. Sens.

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.Surface cover diversity and the complexity of geological structures can seriously impact the accuracy of mineral mapping. To address this issue, we propose a method for lithological classification and analysis based on random forest (RF) and multiple features. Feature vectors, including spectral, polarization, texture, and terrain features, are constructed to provide multidimensional information. Subsequently, these feature vectors are screened based on their discriminative properties for different lithologies to reduce feature redundancy. Finally, the results of lithological classification can be obtained using the RF algorithm based on the selected features. In the experiments conducted in the Qulong copper deposit area, data from Sentinel-1A, Sentinel-2A, and Terra satellites were used to extract multidimensional features. After calculating the Bhattacharyya distance and analyzing the probability density distribution, 17 features selected were input into the RF classifier, achieving an accuracy of 88.83% in lithological classification. This represents a 7.5% improvement compared to exclusively relying on spectral features, and suggests that the proposed method of combining spectral, polarization, texture, and terrain features provides new possibilities for improving the accuracy of field lithological classification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Chen,

2023

J. Appl. Rem. Sens.

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“…e random forest OOB estimation method is an integrated machine learning method using decision trees as base classifiers [28,29]. Its training steps are as follows:…”

Section: Feature Importance Metricmentioning

confidence: 99%

Quantitative Analysis of Broken Rotor Bars in Cage Motor Based on Energy Characteristics of Vibration Signals

Shi

Shen

Ding

2022

Computational Intelligence and Neuroscience

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The rotor, as the power output device of a cage motor, is subject to a type of invisible fault, BRB, during long-term use. The conventional motor vibration signal fault monitoring system only analyzes the rotor qualitatively for the fault of BRBs and cannot evaluate the fault degree of BRBs quantitatively. Moreover, the vibration signal used for monitoring has nonstationary and nonlinear characteristics. It is necessary to manually determine the time window and basis function when extracting the characteristics of the time-frequency domain. To address these problems, this paper proposes a method for quantitative analysis of BRBs based on CEEMD decomposition and weight transformation for feature extraction and then uses the AdaBoost to construct a classifier. The method applies CEEMD for adaptive decomposition while extracting IMFs’ energy as the initial feature values, uses OOB for contribution evaluation of features to construct weight vectors, and performs a spatial transformation on the original feature values to expand the differences between the feature vectors. To verify the effectiveness and superiority of the method, vibration signals were collected from motors in four BRB states to produce rotor fault data sets in this paper. The experiment results show that the feature extraction method based on CEEMD decomposition and weight transformation can better extract the feature vectors from the vibration signals, and the constructed classifier can accurately perform quantitative analysis of BRB fault.

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“…In many applied analyses, the results of the feature selection process, which are usually provided in the form of feature importance rankings [12][13][14][15], or a listing of the features contained in the optimized feature set [9,10], are typically included as part of the analysis results, often with the intention of providing insights on selecting image features for use in future analyses. While feature selection results can be useful for gaining insights on which image features were useful or beneficial for the classification, it is unclear if such results can be extended to other classification models trained from different training sets, or even other, similar remotely sensed data.…”

Section: Introductionmentioning

confidence: 99%

Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification

Ramezan

2022

Remote Sensing

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Remote sensing analyses frequently use feature selection methods to remove non-beneficial feature variables from the input data, which often improve classification accuracy and reduce the computational complexity of the classification. Many remote sensing analyses report the results of the feature selection process to provide insights on important feature variable for future analyses. Are these feature selection results generalizable to other classification models, or are they specific to the input dataset and classification model they were derived from? To investigate this, a series of radial basis function (RBF) support vector machines (SVM) supervised machine learning land cover classifications of Sentinel-2A Multispectral Instrument (MSI) imagery were conducted to assess the transferability of recursive feature elimination (RFE)-derived feature sets between different classification models using different training sets acquired from the same remotely sensed image, and to classification models of other similar remotely sensed imagery. Feature selection results for various training sets acquired from the same image and different images widely varied on small training sets (n = 108). Variability in feature selection results between training sets acquired from different images was reduced as training set size increased; however, each RFE-derived feature set was unique, even when training sample size was increased over 10-fold (n = 1895). The transferability of an RFE-derived feature set from a high performing classification model was, on average, slightly more accurate in comparison to other classification models of the same image, but provided, on average, slightly lower accuracies when generalized to classification models of other, similar remotely sensed imagery. However, the effects of feature set transferability on classification accuracy were inconsistent and varied per classification model. Specific feature selection results in other classification models or remote sensing analyses, while useful for providing general insights on feature variables, may not always generalize to provide comparable accuracies for other classification models of the same dataset, or other, similar remotely sensed datasets. Thus, feature selection should be individually conducted for each training set within an analysis to determine the optimal feature set for the classification model.

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Research on Classification of Open-Pit Mineral Exploiting Information Based on OOB RFE Feature Optimization

Cited by 7 publications

References 54 publications

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Lithological classification and analysis based on random forest and multiple features: a case study in the Qulong copper deposit, China

Quantitative Analysis of Broken Rotor Bars in Cage Motor Based on Energy Characteristics of Vibration Signals

Transferability of Recursive Feature Elimination (RFE)-Derived Feature Sets for Support Vector Machine Land Cover Classification

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