A comprehensive survey on support vector machine classification: Applications, challenges and trends

The detection of epileptic seizure signals based on fuzzy entropy.

The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and ...

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Spatiotemporal event detection: a review

Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h-1. The best results with SS of 100% and FPR of 0 h-1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.

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Epileptic Seizure Prediction Based on Permutation Entropy.

Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery.

Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

In this paper, we propose a new machine learning algorithm that we named Support Vector Selection and Adaptation (SVSA). Our aim is to achieve the classification performance of the nonlinear support vector machines (SVM) by using only the support vectors of the linear SVM. The proposed method does not require any type of kernels, and requires less computation time compared to the nonlinear SVM. The SVSA algorithm has two steps: selection and adaptation. In the first step, some of the support vectors obtained from linear SVM are selected. Then the selected support vectors are adapted iteratively in the traning algorithm. The proposed method are compared against the linear and nonlinear SVM on sythetic and real remote sensing data. The results show that the proposed SVSA algorithm achieves very close performance to nonlinear SVM without any kernels in less computation time.

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Support vector selection and adaptation for classification of earthquake images

In classification of hyperspectral image, a common challenge is to deal with Hughes phenomenon also known curse of dimensionality, which is caused by high dimension with low samples and resulting in a poor classification performance [1]. There have been many ongoing researches in the literature to mitigate the Hughes phenomenon and accordingly increase the classification performance [2], [3], [4]. Support vector machines (SVM) is the one of the most important algorithm used in the classification of hyper-spectral image which is generally not effected by curse of dimensionality. Although it provides a good generalization ability in classification of hyperspectral dataset, recently, in order to increase the performance of SVM with the limited training data, a recursive feature elimination (RFE) approach based on SVM classifier has been introduced in order to rank the features with respect to their contribution to classification performance [5]. RFE approach utilize the objective function as a feature ranking criterion in order to eliminate the redundant features, and to produce a list of features having more discriminant ability. The experiments in the hyperspectral data classification by SVM also showed that the SVM-RFE method does not affected from the curse of dimensionality even if the number of samples are limited, and the satisfactory classification performance is obtained with using a small number of features [6].

Recursive feature selection based on non-parallel svms and its application to hyperspectral image classification

Support vector machines (SVM) is one of the most important methods which has been frequently used in classification of remote sensing images. The classification performance of the SVM strictly depends on choice of convenient kernel function and its kernel parameters called model selection. In the case that the parameters are not appropriately chosen, SVM may result in relatively poor performance. Therefore, the choice of suitable kernel and its parameters is an important topic in classification problems. In this paper, we studied on the optimal selection of the radial basis kernel parameters of SVM using High Dimensional Model Representation (HDMR) which was recently proposed as an efficient tool to capture the input-output relationships in high-dimensional systems for many problems in science and engineering. The performance of the proposed approach was first analysed with some mathematical functions whose optimums are analytically known in comparison to the grid search method. Different experiments were also conducted with synthetic and hyperspectral datasets. The main advantage of the approach over the grid-search is to require relatively few number of training evaluation and hence less computational time in order to optimize the parameters. Therefore, training time required for SVM is significantly reduced.

Optimization of SVM parameters using High Dimensional Model Representation and its application to hyperspectral images

A linear support vector machine (LSVM) is based on determining an optimum hyperplane that separates the data into two classes with the maximum margin. The LSVM typically has high classification accuracy for linearly separable data. However, for nonlinearly separable data, it usually has poor performance. For this type of data, the Support Vector Selection and Adaptation (SVSA) method was developed, but its classification accuracy is not very high for linearly separable data in comparison to LSVM. In this paper, we present a new classifier that combines the LSVM with the SVSA, to be called the Hybrid SVM and SVSA method (HSVSA), for classification of both linearly and nonlinearly separable data and remote sensing images as well. The experimental results show that the HSVSA has higher classification accuracy than the traditional LSVM, the nonlinear SVM (NSVM) with the radial basis kernel, and the previous SVSA.

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Hybrid SVM and SVSA method for classification of remote sensing images

In the recent years, new techniques so called non-parallel support vector machines (NSVM) have been developed and applied to some synthetic and UCI machine learning data sets, yielding competitive results especially in terms of computational complexity and classification performance compared to classical SVM [1]. In binary classification task, the aim of NSVM is to find two non parallel hyperplanes such that each plane is as close as possible to one of the two classes and also as far as possible from the other class. The study of NSVM algorithms was first began with proximal SVM classification which generates two parallel hyperplanes [2]. Afterwards, it has been demonstrated by several different approaches that classification problems could also be tackled with the use of non-parallel hyperplanes. The first non-parallel hyperplane classifier was introduced by Mangarisan and Wild (2006) named as the generalized eigenvalue proximal support vector machine (GEPSVM) [3]. They removed/dropped the parallelism condition of the generated planes and make the first plane be located as close as possible to one data set while keeping it furthest from the points of the other data set and vice versa. Each proximal plane was found by solving two generalized eigenvalue problems instead of solving a quadratic programming problem as it was required for classical Support Vector Machine. In some cases, better classification accuracy results were achieved with GEPSVM in a short span of time compared to classical support vector machine classification algorithms [4].

Remote sensing image classification by non-parallel SVMs

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