Extension Of Support Vector Machine Research Articles

Double-layered granular soil modulus extraction for intelligent compaction using extended support vector machine learning considering soil-structure interaction

Intelligent Compaction (IC) has been acquiring a growing interest in real-time quality control of compacted soil layers because of its high efficiency and full-area coverage. The current intelligent compaction technology allows the determination of the uniformity level of compaction over large areas according to the dynamic response of the roller. However, accurate real-time determination of the soil modulus during compaction based on roller acceleration has been challenging due to the multi-layered composite nature of the soil and the nonlinearities of the governing dynamic equations of motion and soil response. This study adopts a double-layered soil profile, and a three-dimensional finite element model, accounting for soil-drum interaction, is utilised for the analysis. The isotropic hardening elastoplastic hysteretic model was implemented to simulate the soil behaviour subjected to cyclic loading ranging from small to large strain amplitudes and account for stiffness degradation. The comprehensive dataset composed of the roller acceleration response and ground characteristics is then used to correlate the predicted soil modulus via an advanced machine learning approach. The adopted machine learning method incorporating Gaussian Kernel and Generalised Gegenbauer Kernel functions can reasonably predict the double-layered soil modulus during roller compaction. Additional analyses were conducted to observe the proper training size and number of iterations to achieve real-time quality control to be used by site engineers. Furthermore, the influences of the relative modulus ratio, drum length and top layer modulus on the soil surface dynamic displacement are discussed.

RETRACTED ARTICLE: Prediction and detection of breast cancer text data using integrated EANN and ESVM techniques

Breast cancer is certainly considered one among the harmful disorder amongst all the illnesses in scientific science. It is one of the important motives of demise of various girls everywhere in the world. Most breast cancers begins off evolved while malignant lumps might be a cancerous start to develop from the breast cells. The gift is a singular modality for the prediction of most breast cancers and introduces the proposed algorithms like extended support vector machine and extended artificial neural networks which might be the supervised gadget gaining knowledge of strategies for most breast cancers detection through schooling its attributes. The proposed machine makes use of tenfold pass validation to get a correct outcome. The breast analysis record set is taken from Kaggle, Microsoft Database and UCI gadget gaining knowledge of repository. The proposed studies investigating extended support vector machine (ESVM) and extended artificial neural networks (EANN) the usage of the Kaggle and Google Database Datasets. This paper proposed a hybrid method for most breast cancer analysis through lowering the excessive dimensionality of capabilities, the usage of EANN, after which making use of the brand new decreased function dataset to ESVM. The proposed method received an accuracy of 98.82%, sensitivity of 98.41%, specificity of 99.07% and region beneath the receiver running feature curve of 0.9994. The overall performance of the proposed machine is appraised thinking about accuracy, sensitivity, specificity, fake discovery fee, fake omission fee and Matthews’s correlation coefficient. The method offers higher end result each for schooling and checking out. The proposed strategies finished the accuracy of 98.57% and 97.14% through ESVM and EANN in my opinion in conjunction with the specificity of 95.65% and 92.31% in checking out phase.

Beton-dolgulu çelik tüplü kompozit kısa kolonların nihai yük taşıma kapasitesinin ilgililik vektör makinesine dayalı tahmini için modelleme yaklaşımı

In this paper, the applicability of relevance vector machine (RVM) has been explored to predict the ultimate axial load capacity of concrete-filled steel tube composite stub columns (CFSTCSCs) with circular sections under axial compression loadings. As an extension of support vector machine, RVM employs Bayesian inference to achieve parsimonious solutions for regression and classification. By using MATLAB software and 150 comprehensive experimental data presented in the previous studies, a model to predict the ultimate load of circular CFSTCSCs was developed by properly training the data. Utmost care has been taken in grouping the data for training and validation. About 80% dataset for training and 20% dataset for validation have been used, respectively. The results show that the predicted ultimate axial compression load capacity of CFSTCSC members is comparable with that of the corresponding experimental data and the percentage difference is about ∓11%.

Online Learning and Active Learning: A Comparative Study of Passive-Aggressive Algorithm With Support Vector Machine (SVM)

Passive aggressive online learning is an extension of Support Vector Machine (SVM) to the context of online learning for binary classification. In this paper we consider the application of the algorithm on anomaly labeling for IJCNN 2001 Neural Network Competition dataset from LibSVM dataset repository1 from Ford Research Laboratory. We also work on an improved version of the online learning algorithm called Active learning and we compare both algorithms to that of SVM (from LibSVM library). We propose different experimental setups for comparing the algorithms.

Laplacian large margin distribution machine for semi-supervised classification

Semi-Supervised Learning (SSL) has attracted much attention in the field of machine learning and data mining. As an extension of Support Vector Machine (SVM), the Semi-Supervised Support Vector Machine (S3VM) was proposed for SSL. Recent studies have disclosed that optimising the margin distribution is more crucial than maximising the minimum margin in generating a better classification. However, the existing S3VM models still follow the idea of maximising the minimum margin. Therefore, this paper proposes a novel Laplacian Large margin Distribution Machine (LapLDM) for SSL to enhance the classification performance. This method can optimise the margin distribution by maximising the first-order (margin mean) and minimising the second-order (margin variance) statistics of margins, and exploit the geometry information of marginal distribution embedded in the unlabelled data through the Laplacian regularizer. Then this paper develops a Preconditioned Conjugate Gradient (PCG) algorithm to solve the nonlinear LapLDM model on those regular-scaled data sets and a Stochastic Gradient Descent with Variance Reduction (SVRG) algorithm to solve the linear LapLDM model on those large-scaled data sets. These algorithms can accelerate the implementing efficiencies of proposed models and make them available for those large-scaled problems. Finally, the numerical results on four artificial and fourteen public benchmark data sets demonstrate that the LapLDM is superior to some well-known S3VM models.

On removing potential redundant constraints for SVOR learning

As an extension of support vector machine in ordinal regression problem, support vector ordinal regression (SVOR) finds (r−1) parallel hyperplanes by solving a quadratic programming which has n(r−1) constraints. Here, n and r represent the number of training sample and ranks, respectively. Therefore, it would costs much more time to train SVOR than SVC or SVR. Fortunately, the solution of SVOR is only decided by minor constraints which are associated with non-zero Lagrange multipliers. Other constraints with zero Lagrange multipliers have no influence on the solution. Because a training sample is associated with (r−1) constraints, retaining potential support vector may still induce that many redundant constraints are reserved. In this paper, we try to remove these potential redundant constraints for SVOR learning. For the jth parallel hyperplane, the potential constraints with non-zero Lagrange multipliers are associated with the samples near the jth parallel hyperplane. These samples can be identified by a chain near the jth parallel hyperplane. Then, other constraints for the jth parallel hyperplane can be discarded before learning. The number of the constraints can be reduced to less than 17 percent of the original in our experiments. Additionally, it only executes once to find potential critical constraints. Obviously, it is easy to tune parameters after removing potential redundant constraints. The experimental results on several datasets demonstrate that SVOR becomes much faster after removing potential redundant constraints and the performance does not degrade seriously.

Imbalanced Data Classification for Multi-Source Heterogenous Sensor Networks

Most of the traditional classification algorithms are based on the uniform distribution of samples, and the effect is not ideal when dealing with such data, which mainly shows that the classification results incline to the majority class. Therefore, we propose the imbalanced multi-source heterogeneous data classification algorithms in this paper, which are mainly based on the expansion and extension of Support Vector Machines. Considering that there are complex connections within multi-source data, express them as a unified, concise and efficient mathematical model can completely retain data information and improve data processing efficiency. We perform tensor representation and feature extraction on the heterogeneous data, and two different classification algorithms are proposed in this paper. In the first method, we represent multi-source heterogeneous data into a unified tensor form directly and obtain a high-quality core data through dimensionality reduction algorithm, then realize data classification by Support Tensor Machine. In the other method, we extract data from different data sources and classify them with Ensemble Deep Support Vector Machine (DSVM), which combined three DSVM with different kernel functions. The algorithms are compared on CUAVE data set, which contains two different modalities of sound and picture.

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm.

Prediction of compressive strength of GGBS based concrete using RVM

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

Multi-Task Support Vector Machine for Data Classification

Multi-task Learning (MTL) algorithms aim to improve the performance of several learning methods through shared information among all tasks. One particularly successful instance of multi-task learning is its adaptation to support vector machine (SVM). Recently advances in large-margin learning have shown that their solutions may be misled by the spread of data and preferentially separate classes along large spread directions. In this paper, we propose a novel formulation for multi-task learning by extending the recently published relative margin machine algorithm to the multi-task learning paradigm. The new method is an extension of support vector machine for single task learning. The objective of our algorithm is to obtain a different predictor for each task while taking into account the fact that the tasks are related as well as the spread of the data. We test the proposed method experimentally using real data. The experiments show that the proposed method performs better than existing multi-task leaning with SVM and single-task leaning with SVM.

A New Algorithm to Improve Efficiency of Resource Scheduling in Clouding Computing Based on Extended Support Vector Machine

A high effective scheduling in cloud computing environment is significant to guarantee quality of cloud service. In this paper, Weighted Least Squares Support Vector Machine (WLS-SVM) is introduced to reflect finished time and cost of assignments in cloud computing and can obtain robust estimates for regression through the limited observation. There is a simple and efficient approach to model parameters selection. Some significant parameters of support vector machine are defined to further improve convergence performance of algorithm. Moreover, high effective scheduling will be acquired and service cost may be generated in cloud computing environment. Through the simulation experiment, the validity of the proposed model is demonstrated. The results show that the method has more superior performance than other methods like Least Squares Support Vector Machine (LS-SVM).

A Dynamic Local Path Planning Method for Outdoor Robot Based on Characteristics Extraction of Laser Rangefinder and Extended Support Vector Machine

For dynamic path planning problem under unstructured environment, firstly, successive edge following and least squares method (SEF-LSM) is adopted to extract environment characteristics of laser rangefinder data, and SEF-LSM with logical reasoning (SEF-LSM-LR) is proposed for dynamic obstacles characteristics detection. Furthermore, the perpendicularity (PERP) algorithm is utilized to identify dynamic vehicle, according to the perpendicularity attribute of vehicle. Secondly, all the laser rangefinder scanning points are marked as negative ([Formula: see text]) or positive ([Formula: see text]1), and the scanning points of one dynamic obstacle are marked as the same label. Thirdly, extended support vector machine (ESVM) is designed for outdoor robot local path planning under unstructured environment, which consider the practical start-goal position and heading constraints, robot kinematic constraint, and curvature constraint, moreover, the emergency obstacle is regarded as disturbances during planning processing. Finally, the optimal path is chosen by the shortest distance evaluation function. Lots of outdoor simulations show that the proposed method solve the dynamic planning problem under unstructured environment, and their effectiveness performance are verified for outdoor robot path planning.

On Spectral Unmixing Resolution Using Extended Support Vector Machines

Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing.

Active Learning Using Hint Information.

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Fuzzy Assessment of Spectral Unmixing Algorithms

While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp unmixing accuracy assessment, where unmixing performance is assessed using a mean squared error of the estimated endmember fractions, can be misleading. In this paper, alterative spectral unmixing assessment methods are introduced to account for the uncertainty contained in the spectral measurements and during the ground truth data collection. Two fuzzy measures are developed to assess unmixing performance. One is fuzzy unmixing fraction error for a realistic assessment and the other is pixel level unmixing accuracy to provide a good quantitative understanding of the unmixing success rates spatially. To demonstrate and illustrate how they work, the two fuzzy measures are applied to evaluate the performance of several spectral unmixing methods including both single spectrum based and multiple spectra based algorithms. Crisp assessments and fuzzy results at various tolerance levels are presented and compared. Based on the realistic measures proposed, it is found the recent developed unmixing method with extended Support Vector Machines outperforms other algorithms tested.

Embedded feature-selection support vector machine for driving pattern recognition

In this work, a more efficient and robust driving pattern recognition technique, extended Support Vector Machine (SVM) with embedded feature selection ability, has been introduced. Besides statistical significance, this proposed SVM also takes into account the accessibility and reliability of features during feature selection, so as to enable the driving condition discrimination system to achieve higher recognition efficiency and robustness. The recognition results of this extended SVM are compared with results from standard 2-norm SVM and linear 1-norm SVM, using representative driving cycle data to demonstrate the function and superiority of the new technique.

Application of Gaussian Process to Locational Marginal Pricing Forecasting

In this paper, a new method is proposed for Locational Marginal Pricing (LMP) forecasting in Smart Grid. The marginal cost is required to supply electricity to incremental loads in case where a certain node increases power demands in a balanced power system. LMP plays an important role to maintain economic efficiency in power markets in a way that electricity flows from a low-cost area to high-cost one and the transmission network congestion is alleviated. The power market players are interested in maximizing the profits and minimizing the risks through selling and buying electricity. As a result, it is of importance to obtain accurate information on electricity pricing forecasting in advance so that their desire is reflected. This paper presents the Gaussian Process (GP) technique that comes from the extension of Support Vector Machine (SVM) in a way that hierarchical Bayesian estimation is introduced to express the model parameter as the stochastic variables. The advantage is that the model accuracy of GP is better than others. The proposed method is successfully applied to real data of ISONE(Independent System Operator New England) in USA.

Behavioral signatures related to genetic disorders in autism

BackgroundAutism spectrum disorder (ASD) is well recognized to be genetically heterogeneous. It is assumed that the genetic risk factors give rise to a broad spectrum of indistinguishable behavioral presentations.MethodsWe tested this assumption by analyzing the Autism Diagnostic Interview-Revised (ADI-R) symptom profiles in samples comprising six genetic disorders that carry an increased risk for ASD (22q11.2 deletion, Down’s syndrome, Prader-Willi, supernumerary marker chromosome 15, tuberous sclerosis complex and Klinefelter syndrome; total n = 322 cases, groups ranging in sample sizes from 21 to 90 cases). We mined the data to test the existence and specificity of ADI-R profiles using a multiclass extension of support vector machine (SVM) learning. We subsequently applied the SVM genetic disorder algorithm on idiopathic ASD profiles from the Autism Genetics Resource Exchange (AGRE).ResultsGenetic disorders were associated with behavioral specificity, indicated by the accuracy and certainty of SVM predictions; one-by-one genetic disorder stratifications were highly accurate leading to 63% accuracy of correct genotype prediction when all six genetic disorder groups were analyzed simultaneously. Application of the SVM algorithm to AGRE cases indicated that the algorithm could detect similarity of genetic behavioral signatures in idiopathic ASD subjects. Also, affected sib pairs in the AGRE were behaviorally more similar when they had been allocated to the same genetic disorder group.ConclusionsOur findings provide evidence for genotype-phenotype correlations in relation to autistic symptomatology. SVM algorithms may be used to stratify idiopathic cases of ASD according to behavioral signature patterns associated with genetic disorders. Together, the results suggest a new approach for disentangling the heterogeneity of ASD.

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results.

SVM‐Based Generalized Multifactor Dimensionality Reduction Approaches for Detecting Gene‐Gene Interactions in Family Studies

Gene-gene interaction plays an important role in the etiology of complex diseases, which may exist without a genetic main effect. Most current statistical approaches, however, focus on assessing an interaction effect in the presence of the gene's main effects. It would be very helpful to develop methods that can detect not only the gene's main effects but also gene-gene interaction effects regardless of the existence of the gene's main effects while adjusting for confounding factors. In addition, when a disease variant is rare or when the sample size is quite limited, the statistical asymptotic properties are not applicable; therefore, approaches based on a reasonable and applicable computational framework would be practical and frequently applied. In this study, we have developed an extended support vector machine (SVM) method and an SVM-based pedigree-based generalized multifactor dimensionality reduction (PGMDR) method to study interactions in the presence or absence of main effects of genes with an adjustment for covariates using limited samples of families. A new test statistic is proposed for classifying the affected and the unaffected in the SVM-based PGMDR approach to improve performance in detecting gene-gene interactions. Simulation studies under various scenarios have been performed to compare the performances of the proposed and the original methods. The proposed and original approaches have been applied to a real data example for illustration and comparison. Both the simulation and real data studies show that the proposed SVM and SVM-based PGMDR methods have great prediction accuracies, consistencies, and power in detecting gene-gene interactions.