Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data

Feng, Wei; Dauphin, Gabriel; Huang, Wenjiang; Quan, Yinghui; Bao, Wenxing; Wu, Mingquan; Li, Qiang

doi:10.1109/jstars.2019.2922297

Cited by 57 publications

(25 citation statements)

References 36 publications

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“…The SMOTE algorithm [17] is a method for managing unbalanced datasets put forward by Chawla et al in 2002. In the real world, datasets are mainly composed of "normal" samples, with only a small fraction of "abnormal" examples, so the SMOTE algorithm treats the minority "anomalies" by using the method of linear interpolation between the two minority class sample syntheses of new samples, thus effectively relieving the unbalanced data and the effects on the classifier [18][19][20][21]. The proportion of malicious WebShell samples to nonmalicious samples in the datasets in this study is approximately 10:1.…”

Section: Data Sampling Based On the Smote Algorithmmentioning

confidence: 99%

WebShell Attack Detection Based on a Deep Super Learner

Luktarhan

Zhou

et al. 2020

Symmetry

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WebShell is a common network backdoor attack that is characterized by high concealment and great harm. However, conventional WebShell detection methods can no longer cope with complex and flexible variations of WebShell attacks. Therefore, this paper proposes a deep super learner for attack detection. First, the collected data are deduplicated to prevent the influence of duplicate data on the result. Second, to detect the results of the algorithm, static and dynamic feature are taken as the feature of the algorithm to construct a comprehensive feature set. We then use the Word2Vec algorithm to vectorize the features. During this period, to prevent the outbreak of the number of features, we use a genetic algorithm to extract the validity of the feature dimension. Finally, we use a deep super learner to detect WebShell. The experimental results show that this algorithm can effectively detect WebShell, and its accuracy and recall are greatly improved.

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Section: Data Sampling Based On the Smote Algorithmmentioning

confidence: 99%

WebShell Attack Detection Based on a Deep Super Learner

Luktarhan

Zhou

et al. 2020

Symmetry

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“…Random forest (RF) is a standard method of ensemble learning, with the outstanding output of classification and high processing speed, and it can prevent over-fitting effectively [51][52][53][54][55][56][57]. RF is a mixture of tree predictors.…”

Section: Random Forestmentioning

confidence: 99%

Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

et al. 2021

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The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

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“…The problem of class imbalance brings serious challenges to hyperspectral image classification, which reduces the effectiveness of many existing algorithms [5]. Therefore, how to improve the accuracy of minority classes without damaging the accuracy of majority classes is a great challenge [7].…”

Section: Introductionmentioning

confidence: 99%

“…Ensemble learning combines multiple learners to achieve more generalization than a single learner and has been successfully applied to hyperspectral image processing, such as rotation forest (RoF) and its improvement [26]- [29], but most of them are implemented on balanced datasets. In recent years, ensemble learning has been used to solve class imbalanced problem [30], such as dynamic synthetic minority oversampling technique-based rotation forest [7]. Given the superior performance of ensemble learning compared with sub-classifier and the strong feature extraction ability of CNN, this paper will explore a new method combining ensemble learning with CNN to improve imbalanced hyperspectral image classification performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification

Feng

Quan

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral image (HSI) classification often faces the problem of multi-class imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning (DL) has been successfully applied to HSI classification, convolutional neural network (CNN) is one of the most representative of them. However, it is difficult to effectively improve the accuracy of minority classes under the problem of multi-class imbalance. In addition, ensemble learning has been successfully applied to solve multi-class imbalance, such as random forest (RF). This paper proposes a novel ERFS (enhanced random feature subspace)-based ensemble CNN algorithm for the multi-class imbalanced problem. The main idea is to perform random oversampling (ROS) of training samples and multiple data enhancements based on random feature subspace (RFS), and then construct an ensemble learning model combining random feature selection and CNN to HSI classification. Experimental results on three public hyperspectral datasets show that the performance of the proposed method is better than traditional CNN, RF, and deep learning ensemble methods. Index Terms-Hyperspectral image (HSI) classification, multiclass imbalance, enhanced random feature subspace (ERFS), ensemble learning, convolutional neural network (CNN).

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Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data

Cited by 57 publications

References 36 publications

WebShell Attack Detection Based on a Deep Super Learner

WebShell Attack Detection Based on a Deep Super Learner

Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification

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