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The death of the patients is an important event in the intensive care unit (ICU), mortality risk prediction thus offers much information for clinical decision making. However, Patient ICU mortality prediction faces challenges in many aspects, such as high dimensionality, imbalance distribution. In this paper, we modified the cost-sensitive principal component analysis (CSPCA), which is denoted by...
Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each...
The present status of heart sound recognition is introduced in the paper. In order to improve the performance of heart sound recognition, a new model based on SVM is proposed. Firstly, the wavelet transform is used to reduce the noise of the heart sound, and then MFCC feature is extracted from heart sound. On this basis, the Support Vector Machine is used to build the classification model. In the...
At present, shallow characteristics are usually utilized to represent the distributed features of text for Chinese spam classification, causing the problem of inexact text vector representation and low classification performance. A novel Chinese spam classification method based on weighted distributed feature is proposed by combining the features of TF-IDF weighted algorithm with the distributed text-based...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
Automated facial expression recognition (AFER) is a crucial technology to and a challenging task for human computer interaction. Previous methods of AFER have incorporated different features and classification methods and use basic testing approaches. In this paper, we employ the best feature descriptor for AFER by empirically evaluating the feature descriptors named the Facial Landmarks descriptor...
The prediction of the anode effect has long been a challenging industrial issue in aluminum electrolytic production. For improving the prediction precision of the anode effect, this paper combines support vector machine (SVM) and K nearest neighbor (KNN) algorithm. First of all, samples are extracted from the real-time production data and weighted with Relief algorithm. Afterwards, the classifier...
When we use binary tree support vector machine (SVM) to work the multi-classification problems out, we always find that the structure of the binary tree has a large chance and it has a great influence on the classification efficiency of the classification model. To solve this problem, according to the idea of separating the most widely distributed class first, an improved binary tree SVM multiple...
Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the...
The accurate prediction of crude oil output plays an important role in the deployment of oilfield development and ensuring stable production. Crude oil output forecast is the premise and the core project management system of the whole oil production, while crude oil output is a dynamic system affected by multivariate variables. To accurately predict crude oil output, this paper presents a method to...
Advances in data processing, electronics and wireless communications have made the vision of wireless sensor nodes an important reality. Wireless sensor nodes are cheap tiny sensor apparatus integrated with sensing, processing and short-range wireless communication abilities. Recent experimentations have been exploding in terms of usage and performance to improve the way of working in many contexts...
In this paper, a series of spatiotemporal data is analyzed by a regression method based on the theory of support vector machine (SVM). The support vector regression (SVR) model is used to predict the remote sensing data sets effectively. Firstly, we studied how to build a SVR model for spatiotemporal series prediction, and studied the problems of the test data processing, model parameters selection...
High precision forecasting is a prerequisite and guarantee for the operation of grid-connected wind farms. Affected by various environmental factors, wind speed exhibits high fluctuations, autocorrelation and stochastic volatility. Therefore it remains great challenges for short-term wind speed forecasting. To capture its non-stationary property and its tendency, a forecasting model using support...
With the fast development of various methods for image classification using the bag-of-features model, machines can efficiently classify images by image content. Spatial pyramid matching (SPM) for sparse coding to create the dictionary is a popular and very well performing approach for image classification. The linear SPM was proposed to take advantage of the speed of the linear Support Vector Machine...
Pattern recognition techniques have been widely used in security-sensitive applications to distinguish malicious samples from legitimate ones. However, there usually exist some intelligent attackers who intend to have malicious samples to be mis-classified as legitimate at test time, i.e. evasion attack. Current researches show that traditional Support Vector Machines (SVMs) are vulnerable to evasion...
Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
Detecting diseases associated SNPs is the central goal of genetics and molecular biology. However, highthroughput techniques often provide long lists of disease SNPs candidates, and the identification of disease SNPs among the candidates set remains timeconsuming and expensive. In addition, contrasting to the number of SNPs involved, the available datasets (samples) generally have fairly small sample...
Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter...
SVM is an effective method for fault classification. However, it has computational problem while dealing with high dimensional datasets. To overcome this issue, we propose an efficient dimension reduction technique named random projection combined with the SVM for fault classification in this paper. Compared with well known dimension reduction method like PCA, it provides better tradeoff between pairwised...
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