Pixel-Based Object Detection and Tracking with Ensemble of Support Vector Machines and Extended Structural Tensor

Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles applying two different feature-subset selection techniques, namely random selection and k-means clustering, the base classifiers are combined using either majority vote or SVM fusion. Two real-world benchmarks datasets are used to evaluate and compare the performance. Experimental results show that the SVM ensemble of SVM base classifiers using k-means clustering for feature-subset selection and employing an SVM combiner achieve the best classification accuracy, and that feature-subset-selection methods can have considerable impact on the classification accuracy.

In order to develop Human-centric Driver Fatigue Monitoring Systems (HDFMS) with aims to increase driving safety, an efficient combined features extraction from Curvelet transform and Gabor wavelet transform for fatigue expressions descriptions of vehicle drivers is proposed, and Random Subspace Ensemble (RSE) of Support Vector Machines (SVMs) with polynomial kernel as the base classifier is then exploited for classification of three predefined fatigue expressions classes, namely, awake expressions, moderate fatigue expressions, and severe fatigue expressions. The results of holdout and cross-validation experiments show that CF by RSE of SVMs with polynomial kernel outperforms other seven classifiers, i.e., Curvelet features by SVMs classifier, Gabor features by SVMs classifier, CF by five individual SVMs classifiers. With CF and RSE of SVMs with polynomial kernel, the classification accuracies of drivers' fatigue expressions are over 90% in both of the holdout and cross-validation experiments, which show the proposed approach of combined features extraction and RSE of SVMs can be used for developing Human-centric Driver Fatigue Monitoring Systems to increase driving safety.

Online dynamic security assessment of wind integrated power system using SDAE with SVM ensemble boosting learner

In this paper, the algorithm design of the support vector machines (SVMs) ensemble in a practical multi-stage framework is analyzed which can be implemented efficiently by evolutionary multi-objective optimization algorithm. The designing of SVMs ensemble is considered in three stages: first, the bootstrap method and a strategy of dynamical parameter range adjustment are used to generate more diverse base SVMs, and the NSGA-II algorithm which can efficiently tune the parameters of SVMs is applied to ensure the accuracy of base SVMs; Second, the NSGA-II algorithm is used again to select the member of ensemble based on the accuracy and diversity of the ensemble we have measured; Last, the reliability of different class is computed and combined to decide the outputs of ensemble in terms of the decision values of base SVMs. The proposed algorithm is applied to the UCI datasets, some useful results has been concluded for the future work in this field.

Fine-grained classification is a recognition task where subtle differences distinguish between different classes. To tackle this classification problem, part-based classification methods are mostly used. Partbased methods learn an algorithm to detect parts of the observed object and extract local part features for the detected part regions. In this paper we show that not all extracted part features are always useful for the classification. Furthermore, given a part selection algorithm that actively selects parts for the classification we estimate the upper bound for the fine-grained recognition performance. This upper bound lies way above the current state-of-the-art recognition performances which shows the need for such an active part selection method. Though we do not present such an active part selection algorithm in this work, we propose a novel method that is required by active part selection and enables sequential part-based classification. This method uses a support vector machine (SVM) ensemble and allows to classify an image based on arbitrary number of part features. Additionally, the training time of our method does not increase with the amount of possible part features. This fact allows to extend the SVM ensemble with an active part selection component that operates on a large amount of part feature proposals without suffering from increasing training time.

Modeling radiation-induced lung injury risk with an ensemble of support vector machines

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

Cancer recognition with bagged ensembles of support vector machines

Analysis problems of ensemble SVM, such as. Larger scale of the training subsets, strong randomicity and high complexity(time and space), a new ensemble SVM based on RPCL clustering is putted forward. Above of all, the samples are clustered into several clusters according to their distribution with RPCL. Then a small quantity of representative instances is chosen as training sets and training SVM that adopt self-perturbation in population convergence speed. Finally Ensemble Improvement SVM is constructed by relative majority voting. We use complex targets that captured “Man-Car” data from video data and standard benchmark data to test C_PSOSVM. Experiment result illustrate that the algorithm can improve ensemble SVM classification precision and reduce time and space complexity comparing to Bagging, Adaboost.Esp complex targets, C_PSOSVM algorithm classification accuracy can reach 91.24%.KeywordsSupport Vector MachineRPCLEnsemble SVM

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination

Disorders of consciousness (DoC) happen frequently in various brain injuries. Their detection helps timely treatment for better survival outcomes of DoC patients. It is conventionally undertaken via clinical examinations, typically behavioural assessments. However, these neurological examinations consume significant resources of manpower and time, making continuous DoC monitoring practically infeasible. To address this issue, a computer-aided approach is proposed in this article for automated DoC detection through extracting knowledge from electroencephalogram (EEG) signals. It introduces a new connectivity measure: Power Spectral Density Difference (PSDD) incorporating with a recursive Cosine function (CPSDD). Then, the approach classifies brain-injured patients into DoC (i.e., positive) and wakefulness (i.e., negative) classes through an ensemble of support vector machines (EOSVM), which is a type of machine-learning methods. It is further applied to a dataset of 607 patients with brain injuries. Our classification results show that the EOSVM classifier with the new connectivity measure CPSDD has achieved the best classification performance among 12 connectivity measures. For a setting of 97% majority voting from all SVMs, the EOSVM has diagnosed, in high confidence, 35% of patients with the accuracy, sensitivity, and specificity of 98.21%, 100%, and 95.79%, respectively. Thus, the classifier EOSVM incorporating with the new connectivity measure CPSDD is a promising tool for automatic detection of DoC in brain injuries.

The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive fashion with an ensemble of support vector machines. The key idea is to track, evolve, and combine optimum hypotheses over time, based on dynamic optimization processes and ensemble selection. From experimental results, we demonstrate that the proposed strategy is promising, since it outperforms a single classifier variant of the proposed approach and other classification methods often used for incremental learning.

The present work presents the experiments performed with two kinds of ensembles to classify infant cry. The ones selected for testing during the presented experiments are: a boosting ensemble of artificial neural networks and a boosting ensemble of support vector machines. The design and implementation of the ensembles as well as the experiments and some of the results are shown. The experiments are aimed to classify the types of pain -no pain and hunger - no hunger cries

The nodules and the multiple times larger non-nodules of the regions of interested(ROIs) in lung areas are achieved, that would lead to a serious imbalance on the sample data. Many scholars have proposed some algorithms to solve this problem. In this paper, in order to guarantee that there is no correlation among the extracted characteristics, the PCA method is adopted to optimize and reduce dimensions, and then the modified support vector machine(SVM) classifiers using the sequential minimal optimization(SMO) algorithm and the grid research method are proposed to improve the computing efficiency. Furthermore, the abundant lung CT images from the hospital partnership could confirm the experimental results. We compare the classification performance between the ensemble SVM(ESVM) classifier and the cost-sensitive SVM(CSVM) classifier to deal with this problem. Experimental results show the performance of the CSVM classifier based on grid search is satisfactory than the ESVM.

A natural way to determine the knowledge embedded within connectionist models is to generate symbolic rules. Nevertheless, extracting rules from Multi Layer Perceptrons (MLPs) is NP-hard. With the advent of social networks, techniques applied to Sentiment Analysis show a growing interest, but rule extraction from connectionist models in this context has been rarely performed because of the very high dimensionality of the input space. To fill the gap we present a case study on rule extraction from ensembles of Neural Networks and Support Vector Machines (SVMs), the purpose being the characterization of the complexity of the rules on two particular Sentiment Analysis problems. Our rule extraction method is based on a special Multi Layer Perceptron architecture for which axis-parallel hyperplanes are precisely located. Two datasets representing movie reviews are transformed into Bag-of-Words vectors and learned by ensembles of neural networks and SVMs. Generated rules from ensembles of MLPs are less accurate and less complex than those extracted from SVMs. Moreover, a clear trade-off appears between rules’ accuracy, complexity and covering. For instance, if rules are too complex, less complex rules can be re-extracted by sacrificing to some extent their accuracy. Finally, rules can be viewed as feature detectors in which very often only one word must be present and a longer list of words must be absent.