Sparse elastic net multi-label rank support vector machine with pinball loss and its applications

Sparse pinball twin support vector machines

Lately, multi-label classification (MLC) problems have drawn a lot of attention in a wide range of fields including medical, web, and entertainment. The scale and the diversity of MLC problems is much larger than single-label classification problems. Especially we have to face all possible combinations of labels. To solve MLC problems more efficiently, we focus on three kinds of locality hidden in a given MLC problem. In this paper, first we show how large degree of locality exists in nine datasets, then examine how closely they are related to labels, and last propose a method of reducing the problem size using one kind of locality.

Twin support vector machine (TSVM) has been widely applied to classification problems. But TSVM is sensitive to outliers and is not efficient enough to realize feature selection. To overcome the shortcomings of TSVM, we propose a novel sparse twin support vector machine with the correntropy-induced loss (C-STSVM), which is inspired by the robustness of the correntropy-induced loss and the sparsity of the \(\ell _1\)-norm regularization. The objective function of C-STSVM includes the correntropy-induced loss that replaces the hinge loss, and the \(\ell _1\)-norm regularization that can make the decision model sparse to realize feature selection. Experiments on real-world datasets with label noise and noise features demonstrate the effectiveness of C-STSVM in classification accuracy and confirm the above conclusion further.

Melt index is one of the most important quality variables to determine product specifications in the polymer processing, which cannot be real-time measured by ordinary method. In order to estimate the melt index, a soft sensor method using sparse least square support vector machine based on genetic algorithm is proposed. In the proposed method, genetic algorithm is used to realize the sparseness of least square support vector machine and the optimization of its parameters. Simulation results of industrial data show that the proposed method can reduce the model scale which has better generalization capability than that of the non-sparse model.

The importance of classification in machine learning is increasingly acknowledged in contemporary research and applications, such as disease detection, user analysis, etc. However, the efficacy of traditional classification algorithms is frequently affected by challenges such as class imbalance and the processing of large-scale dynamic data. To address these issues, inspired by the cost-sensitive learning strategy (Pinball loss) and instance-level loss function (Focal loss), this study constructs instance-level cost-sensitive loss functions by expanding the sparse and robust Hinge and Ramp loss. The new loss functions can better discern the difference between classes and samples. By integrating with the online SVM classification algorithm and using online gradient descent (OGD) algorithm, it can effectively deal with classification problems on class imbalanced data streams. Numerical experiments on UCI benchmark datasets validate their effectiveness

Effective common spatial pattern (CSP) feature extraction for motor-imagery (MI) EEG recordings usually depends on the filter band selection to a large extent. However, the most proper filter band can hardly be determined manually due to its subject-specific property. This study introduces a sparse support vector machine (SSVM) approach to implement simultaneous CSP feature selection and classification for MI-based brain–computer interface (BCI). In SSVM, CSP features are first extracted on multiple signals that are filtered from raw EEG data at a set of overlapping subbands. SVM with l 1-norm regularization is then proposed to classify MI tasks with automatic selection of filter bands giving the significant CSP features. The effectiveness of SSVM for MI classification is demonstrated on the BCI Competition III dataset IVa, in comparison with several other competing methods. Experimental results indicate that the proposed SSVM method is promising for development of an improved MI-based BCI.

15 - Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification

Scene classification based on single-layer SAE and SVM

The primary aim is to develop a model that can achieve high accuracy in solving multi label classification problems by training, testing, and analyzing deep learning models that utilize both image and text data. In this paper, we propose a novel hybrid model that jointly considers textual and visual data for the task of movie genre prediction, which is a representative example of multi label classification problems. In the proposed model, textual features extracted from movie summaries are obtained using the DistilBERT model, while visual features derived from movie posters are extracted using the ConvNeXt deep learning model. These features from the two modalities are then combined using the XGBoost machine learning algorithm to perform genre prediction. This approach aims to achieve higher accuracy and better generalizability in movie genre classification by integrating information from different modalities through a late fusion method. The ConvNeXt architecture was adapted to the problem using transfer learning and fine-tuning techniques. To achieve the highest performance from the DistilBERT model, optimization was performed for the token length and threshold hyperparameters, and the model with a token length of 256 and a threshold of 0.5 was used in the hybrid model. Furthermore, to maximize the overall performance of the novel hybrid model, optimization was conducted using the Grid Search algorithm. All three proposed models were trained and tested on a dataset obtained from the IMDB website. The performances of the models were evaluated using hamming loss, precision and F1 score metrics. Experimental results revealed that, overall, the text-based model outperformed the image-based model, while the proposed hybrid model achieved higher performance than both individual models. It was demonstrated that textual and visual features complement each other and positively enhance the overall performance. This paper presents an original and effective study for multi label movie genre classification, combining the fields of computer vision, natural language processing, and machine learning.

Due to the limited resolution of hyperspectral sensors, anomalous targets expressed with subpixels are often mixed with nonhomogeneous backgrounds. This fact makes anomalies difficult to be distinguished from the surrounding background. From this perspective, we propose a novel hyperspectral anomaly detection (AD) algorithm based on low-rank & sparse matrix decomposition (LRaSMD) and support vector machine (SVM). First, based on the LRaSMD technique, the Go decomposition (GoDec) model is utilized to decompose the original image into three components: background, anomalies and noise. In this way, the robust background can be obtained. Subsequently, a clustering algorithm is employed to pick some obvious anomalies. Accordingly, we use both samples of background and anomaly to train an SVM model. The original dataset is sent into the SVM model and both anomalous components and background components can be classified. Experiments on a synthetic hyperspectral image validate the performance of the proposed method.

Objectives: Brain arteriovenous malformation (AVM) is one of the most common causes of intracranial hemorrhage in young adults, and its expeditious diagnosis on digital subtraction angiography (DSA) is essential for clinical decision-making. This paper firstly proposed a deep learning network to extract vascular time-domain features from DSA videos. Then, the temporal features were combined with spatial radiomics features to build an AVM-assisted diagnosis model.Materials and method: Anteroposterior position (AP) DSA videos from 305 patients, 153 normal and 152 with AVM, were analyzed. A deep learning network based on Faster-RCNN was proposed to track important vascular features in DSA. Then the appearance order of important vascular structures was quantified as the temporal features. The structure distribution and morphological features of vessels were quantified as 1,750 radiomics features. Temporal features and radiomics features were fused in a classifier based on sparse representation and support vector machine. An AVM diagnosis and grading system that combined the temporal and spatial radiomics features of DSA was finally proposed. Accuracy (ACC), sensitivity (SENS), specificity (SPEC), and area under the receiver operating characteristic curve (AUC) were calculated to evaluate the performance of the radiomics model.Results: For cerebrovascular structure detection, the average precision (AP) was 0.922, 0.991, 0.769, 0.899, and 0.929 for internal carotid artery, Willis circle, vessels, large veins, and venous sinuses, respectively. The mean average precision (mAP) of five time phases was 0.902. For AVM diagnosis, the models based on temporal features, radiomics features, and combined features achieved AUC of 0.916, 0.918, and 0.942, respectively. In the AVM grading task, the proposed combined model also achieved AUC of 0.871 in the independent testing set.Conclusion: DSA videos provide rich temporal and spatial distribution characteristics of cerebral blood vessels. Clinicians often interpret these features based on subjective experience. This paper proposes a scheme based on deep learning and traditional machine learning, which effectively integrates the complex spatiotemporal features in DSA, and verifies the value of this scheme in the diagnosis of AVM.

Sparse discriminant twin support vector machine for binary classification

Most industrial gases such as methane(CH₄), ethylene (C₂H₄) and sulfur hexafluoride (SF6) have obvious absorption characteristics in the infrared band. The infrared absorption spectrum of leaking gas can be obtained through multispectral or hyper-spectral detection technologies to realize gas identification. However, these methods need a lot of work calibrating the detector response curve to target gas. In this work, a sparse infrared absorption spectrum based support vector machine (SVM) recognition method is proposed to obtain the gas absorption peak information without response curve calibration. An uncooled infrared imaging component is utilized to compose a multi-broadband long-pass differential filter infrared imaging setup that filters in the range of 7.5μm ~13.5 μm. Data extracted from multi-band infrared images of C2H4 and SF6 collected by the setup, combined with the simulated data generated by the simulated sparse spectrum algorithm, constitute training set to SVM. C₂H₄ and SF₆ can be accurately identified under laboratory conditions with the path-concentration of 500 ppm·m ~1000 ppm·m. The easy to implement and cost-effective method is expected to realize real-time identification of leaking gas.

This paper addresses the problem of feature selection for the classification of ground penetrating radar signals. We propose a new classification approach based on time-frequency analysis and sparse kernel feature selection. In the proposed approach, a time-frequency or a time-scale transform is first applied to the one-dimensional radar trace. Sparse kernel feature selection is then employed to extract an optimum set of features for classification. The sparse kernel method is formulated as an underdetermined linear system in a high-dimensional space, and the category labels of the training samples are used as measurements to select the most informative features. The proposed approach is evaluated through an industrial application of assessing railway ballast fouling conditions. Experimental results show that the proposed combination of sparse kernel feature selection and support vector machine classification yields very high classification rates using only a small number of features.

Capturing CO2 from the flue gas of a power plant based on chemical absorption method is an effective way to cut down the emission of CO2. However, the principal components in the solvent are very difficult to be measured online. Therefore an online measurement and control system was designed for the process of capturing CO2 by MEA chemical absorption. The feature selection was done according to Bayesian networks. Furthermore based on the principle of a new sparse support vector machine (SLS-SVM), the measurement of ionic species distribution in the absorption solution was predicted, and the results agreed well with the data obtained by Nuclear Magnetic Resonance (NMR) measurement. The mean square error is 3.8645×10-7. The measurement of solution species distribution was carried out online by using infrared sensors and NMR technology. Meanwhile the sampling system and NMR unit were linked for the online analysis based on variable-temperature and variable-pressure NMR technology. Finally a measurement and control platform was constructed based on VI technology. It shows that the performance of traditional equipment can be improved by the novel system, which will provide a more effective method for CO2 capture process.