Landmine detection improvement using one-class SVM for unbalanced data

Tbarki, Khaoula; Said, Salma Ben; Ksantini, Riadh; Lachiri, Zied

doi:10.1109/atsip.2017.8075597

Cited by 6 publications

(2 citation statements)

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“…Then the support vector machine was used to complete the final prediction. Given that our adopted benchmark data sets are unbalanced which may influence the classification effects of support vector machine [37], we used an oversampling approach to balance the data sets in the study. Compared with other experimental results with the same support vector machine classifier, the experimental results show that the proposed method can not only simplify the feature extraction process and reduce the time and space complexity of the classifier but also reflect the sequence features more comprehensively and improve the classification performance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

Chen

et al. 2019

BioMed Research International

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The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

Chen

et al. 2019

BioMed Research International

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“…When training on an unbalanced data set, the algorithm can balance errors. In addition, researchers have also used single classification algorithms to solve the problem of class imbalance, such as one-class SVM, which treats samples of the minority class as novel points, learns the samples of the majority class, and detects so-called novel points [38].…”

Section: Related Workmentioning

confidence: 99%

A Method for Class-Imbalance Learning in Android Malware Detection

Guan

Mao

2021

Electronics

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More and more Android application developers are adopting many different methods against reverse engineering, such as adding a shell, resulting in certain features that cannot be obtained through decompilation, which causes a serious sample imbalance in Android malware detection based on machine learning. Hence, the researchers have focused on how to solve class-imbalance to improve the performance of Android malware detection. However, the disadvantages of the existing class-imbalance learning are mainly the loss of valuable samples and the computational cost. In this paper, we propose a method of Class-Imbalance Learning (CIL), which first selects representative features, uses the clustering K-Means algorithm and under-sampling to retain the important samples of the majority class while reducing the number of samples of the majority class. After that, we use the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to generate minority class samples for data balance, and finally use the Random Forest (RF) algorithm to build a malware detection model. The result of experiments indicates that CIL effectively improves the performance of Android malware detection based on machine learning, especially for class imbalance. Compared with existing class-imbalance learning methods, CIL is also effective for the Machine Learning Repository from the University of California, Irvine (UCI) and has better performance in some data sets.

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