Probabilistic Support Vector Machine Research Articles

IGATE analysis improves the interpretability of single-cell immune landscape of influenza infection.

Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We have developed a new analytical approach, in silico gating annotating training elucidating (iGATE), based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared with BALB/c was associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Furthermore, we demonstrate that the most prominent effect of aging is a defective T cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibited differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varied with host genotype or aging. The software is available at https://github.com/UmichWenLab/iGATE.

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. For patients with advanced NSCLC that do not have oncogene addiction, the preferred treatment approach is a combination of immunotherapy and chemotherapy. However, the progression-free survival (PFS) typically ranges only from about 6 to 8 months, accompanied by certain adverse events. In order to carry out individualized treatment more effectively, it is urgent to accurately screen patients with PFS for more than 12 months under this treatment regimen. Therefore, this study undertook a retrospective collection of pulmonary CT images from 60 patients diagnosed with NSCLC treated at the First Affiliated Hospital of Wenzhou Medical University. It developed a machine learning model, designated as bSGSRIME-SVM, which integrates the rime optimization algorithm with self-adaptive Gaussian kernel probability search (SGSRIME) and support vector machine (SVM) classifier. Specifically, the model initiates its process by employing the SGSRIME algorithm to identify pivotal image features. Subsequently, it utilizes an SVM classifier to assess these features, aiming to enhance the model's predictive accuracy. Initially, the superior optimization capability and robustness of SGSRIME in IEEE CEC 2017 benchmark functions were validated. Subsequently, employing color moments and gray-level co-occurrence matrix methods, image features were extracted from images of 60 NSCLC patients undergoing immunotherapy combined with chemotherapy. The developed model was then utilized for analysis. The results indicate a significant advantage of the model in predicting the efficacy of immunotherapy combined with chemotherapy for NSCLC, with an accuracy of 92.381% and a specificity of 96.667%. This lays the foundation for more accurate PFS predictions and personalized treatment plans.

Research On the Role and Influencing Factors of Momentum in Tennis Matches Based on Linear Conditional Probability Model and Support Vector Machine

This research investigates the role and influencing factors of momentum in tennis matches using a Linear Conditional Probability Model (LCPM) and Support Vector Machine (SVM). The study begins with data preprocessing to address outliers and standardize scoring notations, followed by an analysis of the momentum's role in tennis, which is defined in relation to consecutive points, games won, and service breaks. The principal component analysis (PCA) is employed to synthesize key factors reflecting momentum, including consecutive scores, untouchable shots, aces, and net points, with a focus on their Markovian properties. A momentum formula is constructed, accounting for the sliding average of scoring differentials and the mutual cancellation of momentum between players. The study further examines the stochasticity of swings in play and runs of success, challenging the notion that these elements are random. The results of the Wald Wolfowitz Run Test and Granger Causality Test indicate that momentum significantly influences the probability of winning points and is not a random phenomenon. A PCA-SVM-SHAP model is developed to predict momentum fluctuations, achieving an 81.26% accuracy rate in the validation set. The model identifies net skills, serving skills, and untouchable shots as the most influential factors on momentum changes. The research extends the model's application to unknown tennis matches and other sports events, demonstrating its generalization ability with varying degrees of accuracy.

Probabilistic Combination of Non-Linear Eigenprojections For Ensemble Classification

The emergence of new technologies has changed the way clinicians perform diagnosis. Medical imaging play a crucial role in this process, given the amount of information that they usually provide as non-invasive techniques. Despite the high quality offered by these images and the expertise of clinicians, the diagnostic process is not a straightforward task since different pathologies can have similar signs and symptoms. For this reason, it is extremely useful to assist this process with the inclusion of an automatic tool that reduces the bias when analyzing this kind of images. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify relevant patterns while providing information about the reliability of the classification. Specifically, each image is divided into patches and features contained in each one of them are extracted by applying kernel principal component analysis (PCA). The use of base classifiers within an ensemble allows our system to identify the informative patterns regardless of their size or location. Decisions of each individual patch are then combined according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario where distinguishing between pneumonia patients and controls from chest Computed Tomography (CCT) images, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would highly increase the computational cost) evidence the applicability of our proposal in a real-world environment.

Multispectral imaging for pathogen identification using a filter wheel and smartphone: A frugal innovation approach

To create an affordable clinical test for use in more decentralized settings, we are developing a multispectral imaging system based on a filter wheel and a smartphone. Our application is the label-free identification of uropathogens from images of bacterial colonies directly on a non-chromogenic culture medium. Using feature selection techniques, we have previously shown through calculations, the possibility to move from hyper- to multi- spectral imaging by exploiting less than 8 spectral bands. Here, we confirm our findings by testing a database of true multispectral images acquired using a filter wheel holding up to 22 dichroic bandpass filters with a 10 nm bandwidth. Performance is reported for 5 species using only 6 filters. Probabilistic SVM algorithms were implemented to allow to reject species other than the targeted most prevalent uropathogens as it is crucial to keep the false-positive rate low. Evolution of specificity and sensitivity with probability threshold are discussed in the light of probability frequency distributions.

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods.

Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine

Designing a Comparative Model of Bank Credit Risk Using Neural Network Models, Survival Probability Function and Support Vector Machine

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

SDN has come up as a promising technology for a future network as a logically centralized controlled framework along with its physically distributed architecture isolating the control plane from sending data moving the entire choice capacity to the regulator. SDNs are turning out to be significant because of scalability, adaptability and testing. As SDN needs overhead for operation, it makes it as a target of Distributed Denial of service (DDoS) attacks. The extensive review in the existing literature survey provides results for small footprint of dataset causing over fitting of the classifier. In the survey it is also been observed that the KNN based algorithms to detect DDOS attacks are lazy learners resulting in the noisy data. This paper proposes a Dual Probability Transductive Confidence Machines and Support Vector Machine (DPTCM-SVM) classifier to avoid the over-fitting for detecting DDoS in SDN. The results generated for detection are more than 98% for all the attack classes making it an Eager Learning System which requires less learning space unlike the Lazy Learning Systems.

High-Dimensional Reliability Analysis with Error-Guided Active-Learning Probabilistic Support Vector Machine: Application to Wind-Reliability Analysis of Transmission Towers

Adaptive reliability analysis methods based on surrogate models, especially kriging, have been successfully implemented in many problems. However, the application of kriging is limited to low-dimensional problems with noncategorical performance data. Support vector machine (SVM), by contrast, addresses these limitations, but its application in reliability analysis faces several challenges with regard to robustness, accuracy, and efficiency. This study proposed a new adaptive approach based on probabilistic support vector machine for reliability analysis (PSVM-RA). Different from existing methods that only select training points in the margin of the SVM, the proposed method adopts a new learning function that considers the wrong classification probability for each realization and maximizes the potential for new information offered by a candidate sample for the training set. Moreover, the upper bound of the error that is introduced by the SVM in estimating the failure probability is derived based on a Poisson binomial distribution model considering the likelihood of wrong classification for all the points in the margin of the SVM. This upper bound of error was used in the proposed framework as a stopping criterion to guarantee the desired accuracy. Three numerical examples and an engineering application regarding the wind-reliability analysis of transmission towers were investigated to demonstrate the performance of the proposed method. It was demonstrated that PSVM-RA can provide robust estimates of failure probability when other state-of-the-art methods fail. Moreover, it offers a balance between efficiency and accuracy.

Comparative Study of Multiple Fitting Regression and Bayes and Probabilistic Support Vector Machine Methods in Classification of Single-cell RNA Data

Comparative Study of Multiple Fitting Regression and Bayes and Probabilistic Support Vector Machine Methods in Classification of Single-cell RNA Data

Expectation-based and Quantile-based Probabilistic Support Vector Machine Classification for Histogram-Valued Data

Expectation-based and Quantile-based Probabilistic Support Vector Machine Classification for Histogram-Valued Data

Fusing Landsat-8, Sentinel-1, and Sentinel-2 Data for River Water Mapping Using Multidimensional Weighted Fusion Method

River water extent is critical for understanding river discharge or its hydrological conditions. Although numerous methods have been proposed to map river water from either optical or synthetic aperture radar (SAR) remotely sensed images, uncertainties still exist broadly. In this study, we developed an image fusion method that integrates Landsat-8, Sentinel-1 and Sentinel-2 images simultaneously for river water mapping with two major steps. Firstly, a posterior probability support vector machine model was adopted to generate water probability maps from each individual image; and second, a Multi-dimensional Weighted Fusion Method (MDWFM) was developed to fuse these probability maps. Four reaches with different characteristics were selected as case study sites. High resolution aerial images were acquired and used as the reference to evaluate our results. We found the fusion process not only improves the quality of river water mapping, but also excludes the cloud interference. The fused river water maps become more reliable after the conflicts from difference images being solved by the proposed MDWFM method that contains a proportional conflict redistribution rule. The weighted root mean square difference was reduced to 0.066, and the Area Under the ROC curve reached up to 0.984. The Critical Success Index, Kappa Coefficient, and F-measure reached up to 0.810, 0.836 and 0.895, respectively. These stable and accurate river extent mapping results obtained through fusing multiple images with high spatial resolution (10 m) and short revisit interval (0.4~4.4 days) are of great significance for enriching the data and methodology of hydrological studies.

Automated technique for carotid plaque characterisation and classification using RDWT in ultrasound images

ABSTRACT In this manuscript, a novel method of rational-dilation wavelet transform (RDWT) is proposed for carotid plaque characterisation and classification in Ultrasound images. RDWT is mainly utilised for image acquisition, pre-processing, feature extraction and ensemble classification in automated plaque classification. Here, the transition bands are constructed from the transition function. The statistical features, viz mean, standard deviation, skewness, Renyi entropy, energy are extracted from the sub-bands of RDWT. The Salp Swarm Algorithm (SSA) is mainly used for selecting the optimum features. In this for selecting optimum features using SSA algorithm two conditions are satisfied such as, in the first approach, mean, standard deviation, skewness are selected and then utilised for converting the continual version of salp swarm algorithm to binary. Subsequently, the crossover operator is utilised to select the Renyi entropy, energy features including transfer functions for replacing the average operator and enhancing the characteristics of research method. Plaque Classification uses K Nearest Neighbour (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifiers. Experimental outcomes show the efficiency of the proposed method depending on accuracy, specificity and sensitivity. The proposed method attains accuracy of 93%, sensitivity of 90% and specificity of 94% when likened to the existing techniques, such as KNN, PNN and SVM.

Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips

Unbalanced distribution of trips during the time is one of the factors influencing traffic congestion at some hours of a day. Identifying the significant factors on travelers’ departure time choice and predicting their behavior helps maintain the balance in the time distribution of trips. For this purpose, this study employs and compares two machine learning and probabilistic approaches to model the departure time choice, including four choices, morning peak, noon peak, evening peak, and non-peak hours. Probabilistic support vector machine (PSVM) and multinomial logit (MNL) models calibrated based on the origin–destination data of Qazvin, and the evaluation and comparison of these two models made based on two applications of identifying the significant factors on the departure time and predicting the departure time. In terms of interpretability, the MNL model results have an indisputable advantage due to the lack of interpretable coefficients and parameters in the PSVM model. On the other hand, machine learning models’ predictive power partially covers the disadvantage of not being interpretable. The results show that the PSVM model can predict the departure time with 53.96% accuracy than the 49.98% accuracy of the MNL model. The maximum balanced accuracy for predicting morning peak, noon peak, and non-peak options is 69%, 53%, and 60%, respectively; obtained by the PSVM model and the MNL model predicts the evening peak option with a balanced accuracy of 52% more accurate than PSVM.

Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying deep myometrial invasion (DMI) using MRI. Moreover, these existing methods exhibit relatively unsatisfactory sensitivity and specificity. This study proposes a novel computerized method to facilitate the accurate detection of DMI on MRI. This method requires only the corpus uteri region provided by humans or computers instead of the tumor region. We also propose a geometric feature called LS to describe the irregularity of the tissue structure inside the corpus uteri triggered by EC, which has not been leveraged for the DMI prediction model in other studies. Texture features are extracted and then automatically selected by recursive feature elimination. Utilizing a feature fusion strategy of strong and weak features devised in this study, multiple probabilistic support vector machines incorporate LS and texture features, which are then merged to form the ensemble model EPSVM. The model performance is evaluated via leave-one-out cross-validation. We make the following comparisons, EPSVM versus the commonly used classifiers such as random forest, logistic regression, and naive Bayes; EPSVM versus the models using LS or texture features alone. The results show that EPSVM attains an accuracy, sensitivity, specificity, and F1 score of 93.7%, 94.7%, 93.3%, and 87.8%, all of which are higher than those of the commonly used classifiers and the models using LS or texture features alone. Compared with the methods in existing studies, EPSVM exhibits high performance in terms of both sensitivity and specificity. Moreover, LS can achieve an accuracy, sensitivity, and specificity of 89.9%, 89.5%, and 90.0%. Thus, the devised geometric feature LS is significant for DMI detection. The fusion of LS and texture features in the proposed EPSVM can provide more reliable prediction. The computer-aided classification based on the proposed method can assist radiologists in accurately identifying DMI on MRI.

Probabilistic Reconstruction of Hydrofacies With Support Vector Machines

AbstractDelineation of geological features from limited hard and/or soft data is crucial to predicting subsurface phenomena. Ubiquitous sparsity of available data implies that the reliability of any delineation effort is inherently uncertain. We present probabilistic support vector machines (pSVM) as a viable method for both hydrofacies delineation from sparse data and quantification of the corresponding predictive uncertainty. Our numerical experiments with synthetic data demonstrate an agreement between the probability of a pixel classifier predicted with pSVM and indicator Kriging. While the latter requires manual inference of a variogram (two‐point correlation function) from spatial observations, pSVM are highly automated and less data intensive. We also investigate the robustness of pSVM with respect to its hyper‐parameters and the number of measurements. Having investigated these features of pSVM, we deploy them to delineate, from lithological data collected in a number of wells, the spatial extent of an aquitard separating two aquifers in Southern California.

Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

Recently Inverted Beta-Liouville mixture models have emerged as an efficient paradigm for proportional positive vectors modeling and unsupervised learning. However, little attention has been devoted to investigate these generative models within discriminative classifiers. Our aim here is to reveal the structure of non-Gaussian data by generating new probabilistic SVM kernels from inverted-Beta Liouville mixture models. The inverted Beta-Liouville has a more general covariance structure and a smaller number of parameters than the inverted Dirichlet and generalized inverted Dirichlet, respectively, which makes it more practical and useful. A principled Bayesian learning algorithm is developed to accurately estimate the model’s parameters. To cope with the problem of selecting the optimal number of components, we further propose a nonparametric Bayesian learning algorithm based on an extended infinite mixture model which may have better modelling and clustering capabilities than the finite model for some applications. Finally, the resulting generative model is exploited to build several efficient probabilistic SVM kernels in order to enhance the expected clustering and modeling performance. Through a number of experimental evaluations involving visual scenes classification, text categorization and texture images discrimination, we prove the merits of the proposed work.

An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine

Support vector machine (SVM), which serves as one kind of artificial intelligence technique, has been widely employed in transformer fault diagnosis when involving dissolved gas analysis (DGA). However, when using SVM, it is easy to misclassify samples which are located near the decision boundary, resulting in a decrease in the accuracy of fault diagnosis. Given this issue, this paper proposed a genetic algorithm (GA) optimized probabilistic SVM (GAPSVM) integrated with the fuzzy three-ratio (FTR) method, in which the GAPSVM can judge whether a sample is near the decision boundary according to its output probabilities and diagnose the samples which are not near the decision boundary. Then, FTR is used to diagnose the samples which are near the decision boundary. Combining GAPSVM and FTR, the integrated model can accurately diagnose samples near the decision boundary of SVM. In addition, to avoid redundant and erroneous features, this paper also used GA to select the optimal DGA features. The diagnostic accuracy of the proposed GAPSVM integrated with the FTR fault diagnosis method reached 86.80% after 10 repeated calculations using 118 groups of IEC technical committee (TC) 10 samples. Moreover, the robustness is also proven through 30 groups of DGA samples from the State Grid Co. of China and 15 practical cases with missing values.

Concentration-Emission Matrix (CEM) Spectroscopy Combined with GA-SVM: An Analytical Method to Recognize Oil Species in Marine.

The establishment and development of a set of methods of oil accurate recognition in a different environment are of great significance to the effective management of oil spill pollution. In this work, the concentration-emission matrix (CEM) is formed by introducing the concentration dimension. The principal component analysis (PCA) is applied to extract the spectral feature. The classification methods, such as Probabilistic Neural Networks (PNNs) and Genic Algorithm optimization Support Vector Machine (SVM) parameters (GA-SVM), are used for oil identification and the recognition accuracies of the two classification methods are compared. The results show that the GA-SVM combined with PCA has the highest recognition accuracy for different oils. The proposed approach has great potential in rapid and accurate oil source identification.

Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>