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Since the crude oil market can make an impact to global economics, it is important to develop some effective approaches to forecast crude oil price and its volatility. In this paper, the goal is to predict the tendency of crude oil future price from ten selected features that potentially affect the crude oil price. Currently, the most popular and robust prediction methods are based on machine learning,...
Many computer-based devices are now connected to the internet technology. These devices are widely used to manage critical infrastructure such energy, aviation, mining, banking and transportation. The strategic value of the data and the information transmitted over the Internet infrastructure has a very high economic value. With the increasing value of the data and the information, the higher the...
Outlier detection is a method to improve performances of machine learning models. In this paper, we use an outlier detection method to improve the performance of our proposed algorithm called decision boundary making (DBM). The primary objective of DBM algorithm is to induce compact and high performance machine learning models. To obtain this model, the DBM reconstructs the performance of support...
Predicting criminal recidivism effectively is of major interest in criminology. In this paper, we study the ability of the support vector machines (SVM) to predict the probability of reincarceration. As a semi parametric approach, the SVM minimizes structural risk whereas nonparametric models, such as neural networks, minimize empirical risk. Furthermore, the SVM differs significantly from existing...
Forecasting the tax gross exactly is significant to carry on the macroscopic regulation efficiently under the market economy. Conventional linear macroscopic economic model is very difficult to hold non-linear phenomena in economic system, thus the tax forecasting error will increase. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small...
Prediction of urban water demand is significant to urban water supply and treatment. To forecast urban water demand exactly, support vector machine optimized by genetic algorithm (GA-SVM) is proposed. Genetic algorithm(GA) is used to determine training parameters of support vector machine in GA-SVM. The experimental results indicate that the proposed GA-SVM model not only requires small training data,...
A classification model is obtained after a classifier is trained on training data. Decision region is the region in which data are predicted the same class label by a classifier. Decision boundary is the boundary between regions of different classes. We view classification as dividing the data space into decision regions. The formal definitions of decision region and decision boundary are presented...
Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and business community. Because in credit scoring areas we usually cannot label on customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, in this paper, we apply a fuzzy membership to each...
Support vector machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the problem of learning from imbalanced data sets in which negative instances greatly outnumber the positive instances. This paper analyzes the intrinsic factors behind this failure and proposes a suitable re-sampling method. We...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
To Solve the problem of low accuracy and high false alarm, a construction method of Bagging ensemble based on random subspace PCA (Principle Component Analysis) was proposed. To create a training data for a base classifier, the feature set is randomly split into several subsets and PCA is applied to each subset. all principal components are retained to keep the variety information in the data; To...
With citypsilas rapid development, environment and resource problem is more and more prominent. For the sake of achieving the basic aim of the city environmental sustainable development, we need establishing an assessment model for predict the environment effectiveness of China city. In this paper, a new environment effect assessment system for China city is presented. The assessment system based...
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper is classifying the scenes using support vector machine with radial basis kernel with...
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