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This paper develops an integrated framework to forecast the volatility of crude oil prices by considering the impacts of extreme events (structural breaks). The impacts of extreme events are vital to improving prediction accuracy. Aiming to demonstrate the crude oil price fluctuation and the impacts of external events, this paper employs the complementary ensemble empirical mode decomposition (CEEMD). It decomposes the crude oil price into some constituents at various frequencies to extract a market fluctuation, a shock from extreme events and a long-term trend. The shock from extreme events is found to be the most crucial element in deciding the crude oil prices. Then we combine the iterative cumulative sum of squares (ICSS) test with the Chow test to get the structural breaks and analyze the extreme event impacts. Finally, this paper combines the structural breaks, the autoregressive integrated moving average (ARIMA) model, and the support vector machine (SVM) to make a forecast of the crude oil prices. The empirical process proves that the CEEMD-ARIMA-SVM model with structural breaks performs the best when compared with the other ARIMA-type models and SVM-type models. The framework offers an insightful view to help decision-makers and can be used in many areas. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a popular and important machine learning technique based on the central idea of maximizing the minimummargin , i.e., the smallest distance from the training points to the classification boundary. However, recent theoretical studies revealed that maximizing the minimum is not crucial to the generalization performance, while the margindistribution is essential. Furthermore, kernel-free quadratic support vector machine becomes a new trend in the SVM area since it can avoid some difficulties such as finding appropriate kernel functions and tuning their hyper-parameters. This paper proposes two novel non-kernel quadratic surface optimal margin distribution machine (QSODM L and QSODM) models. Their basic idea is to optimize the margin distribution of training points by maximizing the margin mean, minimizing the margin variance and classifying data points by directly constructing a quadratic surface in the original space. These proposed models are convex so that they can be solved by some well-known solvers. Moreover, in order to improve the efficiencies of proposed models on large-scale data sets, a stochastic gradient descent with variance reduction (SVRG) algorithm is proposed to solve the proposed models. The numerical results on some artificial and benchmark data sets verify that QSODM L and QSODM are superior to some other well-known SVM models in classification accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a popular supervised machine learning technique extensively applied to various real-life applications. A weakness of SVM, though, is that its efficiency rapidly decreases as the size of training dataset increases. This issue is more challenging for the devices and applications of IoT where the computing resource is quite constrained. As a result, numerous schemes have been proposed to decrease the training time of SVM, especially for the large-scale dataset. In this paper, a survey on the state-of-the-art methods for reducing the number of training data points and increasing the speed of SVM is presented. The existing methods are categorized into five types based on the approach employed for data reduction process, namely clustering-based, geometrical, distance-based, random sampling, and evolutionary methods. The key characteristics of the schemes are reviewed and compared against six practical benchmarks. This survey will be useful to select an appropriate method for the given target problem and develop a new approach for improving the performance of SVM dealing with the large-scale dataset. [ABSTRACT FROM AUTHOR]

Label-specific features learning is a multi-label learning framework that utilizes label feature extraction to solve a single example where multiple class labels exist simultaneously. As an essential multi-label learning method, label correlation learning has been widely used in multi-label classification learning. However, in the existing label-specific features learning, the label correlation measurement often assumes that the label correlations are a global structure or that the label correlations only have a local smoothness. In actual application scenarios, the two situations may occur together. This paper proposes a multi-label classification method by joint Label-Specific features and Global and Local label correlation learning, named LSGL. Firstly, we obtain the weight of the label-specific features of each class label utilizing the l 1 -norm and then learn high-order global label correlation and label local smoothness. By adding manifold regularization terms, we fully utilize the structural relationship between features and labels and mine global and local label association information. These processes are carried out in a unified optimization model, and each part learns and promotes each other. Finally, in the low-dimensional label-specific features representation learning is to carry out multi-label classification learning through the support vector machine and the extreme learning machine, respectively. A comparative study with state-of-the-art approaches and statistical hypothesis testing manifests the validity of the LSGL method and the features learned from label-specific features learning. [ABSTRACT FROM AUTHOR]

The existence of both uncertainty and imprecision has detrimental impact on efficiency of decision-making applications and some machine learning methods, in particular support vector machine in which noisy samples diminish the performance of SVM training. Therefore, it is important to introduce a special method in order to improve this problem. Fuzzy aspects can handle mentioned problem which has been considered in some classification methods. This paper presents a novel weighted support vector machine to improve the noisy sensitivity problem of standard support vector machine for multiclass data classification. The basic idea is considered to add a weighted coefficient to the penalty term Lagrangian formula for optimization problem, which is called entropy degree, using lower and upper approximation for membership function in fuzzy rough set theory. As a result, noisy samples have low degree and important samples have high degree. To evaluate the power of the proposed method WSVM-FRS (Weighted SVM-Fuzzy Rough Set), several experiments have been conducted based on tenfold cross-validation over real-world data sets from UCI repository and MNIST data set. Experimental results show that the proposed method is superior than the other state-of-the-art competing methods regarding accuracy, precision and recall metrics. [ABSTRACT FROM AUTHOR]

Machine learning methods are increasingly used in proteomics research, especially in analyzing and predicting protein structures, functions, subcellular localizations and interactions. However, much research in recent years has focused on protein misfolding problem and the impact of unfolded and defective proteins on cell dysfunction, due to its considerable importance for molecular medicine. These abnormal proteins degradation and deposition often result in the formation of certain plaque cores among them the so-called amyloid fibrils which are responsible for an increasing number of highly debilitating disorders in humans. Yet, a significant challenge remains, especially in understanding the underlying causes and major risk factors of these harmful deposits in vital organs and tissues. This paper explores the potential of string kernel-based support vector machines in the prediction of amyloidogenic regions in proteins by incorporating the most informative features of the protein sequence such as predicted secondary structure and solvent accessibility, with a special focus on α -helical conformations which seem to be primarily concerned with amyloidogenesis. The performances compared with the most popular methods on Pep424 and Reg33 benchmark datasets indicate the robustness of the predictive model. Furthermore, the results showed accurate prediction of regions promoting fibrillogenesis for experimentally determined amyloid proteins and revealed that the five amino acids Leucine, Glycine, Alanine, Valine and Serine are predominantly present in amyloid-prone regions and confirm that the core regions of an amyloid aggregate are not necessarily fully buried. [ABSTRACT FROM AUTHOR]

Fuzzy neural network (FNN) and support vector machine (SVM) are two prominent and powerful learning models broadly used for classification and regression. FNN has a significant local representation and human reasoning advantage. However, the drawback of such a network is that the focal point of the learning algorithms is minimizing empirical risk. In contrary to FNN, SVM emphasizes simultaneously on minimizing empirical and expected risks, which theoretically leads to an excellent generalization performance power. In this paper, we show that a Takagi–Sugeno–Kang (TSK)-type-based fuzzy neural network is, in fact, equivalent to an SVM with an adaptive kernel based on fuzzy rules generated in this FNN. Consequently, it is possible to learn the last layer of the FNN using the concepts of SVM and thus taking the advantage of SVM in generalization. In fact, on the one side, the proposed method is an SVM with an adaptive kernel based on fuzzy rules and, on the other side, it is a TSK–FNN with SVM-based learning. As a matter of fact, the defined kernel in FNN is an adaptive kernel based on data characteristics, which is derived from the fuzzy rules generated by the FNN itself. The results obtained from the proposed method for classification and regression outperform the results of SVM with the conventional kernels and fuzzy neural network trained in the traditional way. [ABSTRACT FROM AUTHOR]

Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables involved in them. Using machine learning algorithms, we predicted graduation rates in a real case study to support decision making. Real data from five undergraduate engineering programs at District University Francisco Jose de Caldas in Colombia illustrate our results. The comparison between support vector machine and artificial neural network is held using the confusion matrix and the receiver operating characteristic curve. The algorithm methods and architecture are presented. [ABSTRACT FROM AUTHOR]

Local multiple kernel learning is a promising strategy because it could learn a sample-specific composite kernel according to the characteristic of samples. However, these methods are insufficient to describe the sample diversity and correlation, which leads to the classifier less reliable. In this paper, we propose a soft-clustering-based local multiple kernel learning algorithm to tackle the issues above. In the proposed algorithm, there is a fuzzy clustering preprocessing for the training data and then the kernel weights are calculated on the groups. We use an alternative optimization method to learn the kernel weights and support vector coefficients. The final combination weights of kernels are determined by the kernel weights of clusters and the probability of samples falling into the clusters. Therefore, our method is actually a sample-based LMKL method with a soft constraint on the kernel weights. This constraint is actually the representation of the correlation of samples. The experiments on synthetic dataset indicate the kernel weights solved by our algorithm are better suitable for the characteristics of the dataset. Then a series of experiments verify the improvement on classification accuracies. [ABSTRACT FROM AUTHOR]

In this paper, a novel data selection algorithm for identifying significant training data is presented for power quality (PQ) events classification. The key concept of this paper is to reduce the execution time, computational complexity and enhance the accuracy of the existing PQ classification system by reducing the number of support vectors. The proposed scheme identifies important training data and rejects redundant and irrelevant ones using a novel fuzzy C-means clustering-based data selection algorithm thereby reducing time of classification and enhancing accuracy. Significant features from raw PQ data are extracted using discrete wavelet transform and important training data are recognized using these features with the proposed data selection algorithm. Two machine learning algorithms namely the probabilistic neural network and support vector machine are employed and the best PQ classifier is investigated. Furthermore, the proposed data selection algorithm is integrated with the existing PQ classification system that has selected optimal input features and parameters of the classifier using Simulated Annealing and is shown to perform exceptionally well when compared to conventional classifiers that use full training data set. The suitability of the algorithm for noisy as well as real time data is examined and the empirical results show that the proposed scheme performs well compared to several existing PQ classification works. [ABSTRACT FROM AUTHOR]

Learning from categorical data is a critical yet challenging task. Current research focuses on either leveraging the complex interaction between and within categorical values to generate a numerical representation, or designing a model that can tackle this types of data directly. However, both of these paradigms overlook the relation between the data characteristics and learning model hypothesis. In this paper, we propose a model-aware categorical data embedding framework that jointly reveals the intrinsic categorical data characteristics and optimizes the fitness of the representation for the follow-up learning model. An ELM-aware and a SVM-aware representation methods have been instantiated under this framework. Extensive experiments of classification with the embedded representation on 17 data sets demonstrate that the proposed framework can significantly improve the categorical data representation performance compared with state-of-the-art competitors. [ABSTRACT FROM AUTHOR]