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The SVM can realize data classification and prediction, the selection of penalty parameter c and kernel function g in training models directly affect the forecasting accuracy of the classification, the article use the K-CV method for c, g parameters optimization and processing, in wine species identification as an example to predict classification, improves the forecast accuracy, has reached the expected...
Internet and various services offered by it has become a daily routine. The Quality of Web Service (QWS) has become a significant factor in distinguishing the success of service providers. The main purpose of this paper is to analyze quality prediction using the IKS hybrid model with a new approach of data classification. We present the IKS hybrid model. The model combines selection of features, clustering...
The identification of optimal candidates for ventricular assist device (VAD) therapy is of great importance for future widespread application of this life-saving technology. During recent years, numerous traditional statistical models have been developed for this task. In this study, we compared three different supervised machine learning techniques for risk prognosis of patients on VAD: Decision...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
Continuously increasing amounts of data in data warehouses are providing companies with ample opportunity to conduct analytical customer relationship management (CRM). However, how to utilize the information retrieved from the analysis of these data to retain the most valuable customers, identify customers with additional revenue potential, and achieve cost-effective customer relationship management,...
From a new view of financial distress concept drift, this paper attempts to put forward a new method for dynamic financial distress prediction modeling based on slip time window and multiple support vector machines (SVMs). A new algorithm is designed to dynamically select the proper time window to handle concept drift, and then a dynamic classifier selection method is used to build a combined model...
In order to construct a high-performance ensemble classifier, it needs that the basic classifiers, which contained by the ensemble one, have higher classification precision and their classification error is independent from each other. In fact, it is too difficult to choose these basic classifiers satisfying the two conditions above. Rough reduction is the core in the fields of Rough Set theory. Each...
There have been a lot of researches that demonstrate the phenomenon of life or the origin of the disease and classify or diagnose the state of the cell. These are usually achieved by the strength of the gene expression under certain circumstances by the microarray which can observe tens and thousands of gene expression profile. It is not feasible to use all the attributes because a lots of gene expression...
Support vector machine (SVM) is a promising method of machine learning based on the structural risk minimization principle, which is characteristic of good generalization performance; Rough set (RS) is an effective tool to decrease data dimension in dealing with vagueness and uncertainty information. A SVM classifier based on RS reducts is researched in order to enhance the predicting performance...
In this paper, we use particle swarm optimization with support vector machine optimized to evaluate the investment risk of electrical project. A hybrid intelligent system is applied to evaluation of electrical equipment, combining particle swarm optimize algorithm (PSO) and support vector machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general...
In the area of ocean fisheries research, a new research interest is to use marine environment factors for fishery forecasting. This paper proposes a novel knowledge discovery model for fishery forecasting that uses the Indian Ocean big-eye tuna fishery as its testing ground. The model employs a 3-step process. Firstly the support vectors can be obtained by training the support vector machine (SVM)...
There are many unimportant features in the hypertension sample data set in the three gorges area, which are gathered by Tongji medical college, school of HUST. These redundant irrelevant features spoil the classification, increase many unwanted calculations and decrease the real-time capacity of the medical prediction. In order to solve above problem, an improved hypertension prediction model based...
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