Multi-View Least Squares Support Vector Machines Classification

Houthuys, Lynn; Langone, Rocco; Suykens, Johan A. K.

doi:10.1016/j.neucom.2017.12.029

Cited by 68 publications

(34 citation statements)

References 7 publications

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“…We have done extensive experiments with several versions of VGGNet-like architectures for best architecture selection. The experiments showed that the architecture with (16,32,64,128) layers performed best for the feature extraction stage. So, we decided to use this small version of VGGNet architecture for feature extraction using four view input of mammograms, as is shown in Figure 6.…”

Section: ) Feature Extraction Strategymentioning

confidence: 99%

“…We can get useful feature information from all views in different training stages. Combining the data information from all the views before training is called early fusion and can be applied by concatenation of all features [16], [17]. The proposed framework based on the four-view feature fusion technique outperforms state-of-the-art approaches that are evaluated on benchmark datasets: CBIS-DDSM and mini-MIAS.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies other than medical imaging, for instance [16], [26], [27] also have been carried out on multi-view classification machine learning methods. Houthuys et al [16] proposed a classification approach named as multi-view least squares support vector machines. Their main contribution is a coupling term for reducing the combination of error from multiple views.…”

Section: Introductionmentioning

confidence: 99%

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Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

et al. 2019

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Breast cancer is the second most common cause of cancer-related deaths among women. Early detection leads to better prognosis and saves lives. The 5-year survival rate of breast cancer is 99% if it is located only in breast. Conventional computer-aided diagnosis (CADx) systems for breast cancer use the single view information of mammograms to assist the radiologists. More recent work has focused on more than one views. Existing multi-view based CADx systems normally employ only two views namely Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). The information fusion of the two views proved the effectiveness of the system for mammogram classification which cannot be achieved by single view information. However, combining the information of four views of mammograms increases the performance of classification. In this study, we propose Multi-View Feature Fusion (MVFF) based CADx system using feature fusion technique of four views for classification of mammogram. The complete CADx tool contains three stages, the first stage have the ability to classify mammogram into abnormal or normal, second stage is about classification of mass or calcification and in the final stage classification of malignant or benign classification is performed. Convolutional Neural Network (CNN) based feature extraction models operate on each view separately. These extracted features were fused into one final layer for ultimate prediction. Our proposed system is trained on four views of mammograms, after data augmentation. We performed our experiments on publicly available datasets such as CBIS-DDSM (Curated Breast Imaging Subset of DDSM) and mini-MIAS database of mammograms. In comparison with literature the MVFF based system is performed better than a single view-based system for mammogram classification. We have achieved area under ROC curve (AUC) of 0.932 for mass and calcification and 0.84 for malignant and benign, which is higher than all single-view based systems. The value of AUC for normal and abnormal classification is 0.93.

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Section: ) Feature Extraction Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

et al. 2019

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“…SVM is a machine learning method based on statistical learning theory. It applies the principle of structural risk minimization to the classification field [34]. In this study area, there are two main vegetation types, grass, and woods.…”

Section: Methodsmentioning

confidence: 99%

Remote Sensing Assessment of Safety Risk of Iron Tailings Pond Based on Runoff Coefficient

Che

Liang

et al. 2018

Sensors

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Iron tailings ponds are engineered dam and dyke systems used to capture iron tailings. They are high-risk hazards with high potential energy. If the tailings dam broke, it would pose a serious threat to the surrounding ecological environment, residents’ lives, and property. Rainfall is one of the most important influencing factors causing the tailings dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk tailings ponds, 16 moderate-risk tailings ponds, and 4 high-risk tailings ponds in the study area. This method could be useful for selecting targeted tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk tailings ponds in rainy season.

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“…Least squares version for SVM classifiers (LSSVM) was emerged due to equality type constraints in the formulation. Its solution followed from solving a set of linear equations instead of quadratic programming for classical SVM's [30], which has been used to solve different problems such as intelligent diagnosis [31], feature selection [32], multi-view classification [33]. Shao et al [34] proposed a new algorithm of twin bounded support vector machines (TBSVM) based on the twin support vector machine (TWSVM).…”

Section: Related Workmentioning

confidence: 99%

The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance

Zhang

et al. 2019

IEEE Access

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Studies on the traditional support vector machine (SVM) implicitly assume that the costs of different types of mistakes are the same and minimize the error rate. On the one hand, it is not enough for many practical applications to rely solely on the error rate, which reflects only the average classification ability of a classifier. It is also of great significance to consider the performance of classifiers from the perspective of each sample. On the other hand, many real-world problems, such as credit card fraud detection, intrusion detection, oil-spill detection and cancer diagnosis, usually involve substantially unequal misclassification costs. To solve this problem, many works on the cost-sensitive SVM (CS-SVM) have emerged. The misclassification costs for this model are generally provided by domain experts. Inspired by the concept of the CS-SVM, we propose a new SVM with sample-based misclassification cost invariance with the aim of constructing a relatively reliable classifier. The relatively reliable classifier is defined as the one with low probabilities of finding a classifier that correctly classifies each misclassified sample. Note that the cost is determined by the inherent characteristics of each sample rather than being subjectively assigned, so we denote the proposed classifier as the objective-cost-sensitive SVM (OCS-SVM). The experimental results demonstrate the superiority of the proposed method compared with nine other commonly used classifiers. INDEX TERMS Cost-sensitive learning, objective cost, reliability, SVM, total cost.

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Multi-View Least Squares Support Vector Machines Classification

Cited by 68 publications

References 7 publications

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Multi-View Feature Fusion Based Four Views Model for Mammogram Classification Using Convolutional Neural Network

Remote Sensing Assessment of Safety Risk of Iron Tailings Pond Based on Runoff Coefficient

The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance

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