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Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term...
This article proposed ‘TLiSVM’ or ‘3LiSVM’ (Triple Linear SVM Weight) as an alternative technique for dimensionality reduction with a Support Vector Machine (SVM) classifier on a two-class dataset. The efficiency of TLiSVM was compared with two chosen techniques, including Linear SVM Weight (LiSVM) and Double Linear SVM Weight (DLiSVM). Three datasets, including DLBCL, Duke Breast-Cancer and Leukemia,...
In recent years, higher education has been gaining importance in graduate students to make successful careers. So, academic organizations are given utmost importance for quality in academics to build the careers of the students. Faculty performance plays a vital role in academic institutions. In this paper, the performance of faculty members is evaluated on the basis of different parameters are taken...
We present Rough-Fuzzy Support Vector Domain Description (RFSVDD), a novel data description algorithm that provides a rough-fuzzy characterization of a data set and shows its potential for outlier detection. Its resulting data structure is characterized by two components: a crisp lower-approximation and a fuzzy boundary. While the lower-approximation consists of those data points that lie inside the...
As a new kind of social media, query log gains mass size of users and data. It's easy for people to post on query log. Also, the posts spread fast and can be easily seen by many other users. For the reasons above, users post various and large number contents on query log. Among these posts, we find numerous posts that express authors purchase wish for a certain product, in other words, consumption...
Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
Aiming at the difficult measurement problem of the extraction rate for plants and herbs with the ultrasonic wave technology, the influence of the various factors on the extraction rate in the ultrasonic extraction process is analyzed and the dynamic process variables which is easily measured and can affect the extraction rate is ensured in this paper. A soft sensor model between the easily measured...
PLS can effectively eliminate the multicolinearity among explanatory variables and LSSVM can reflect the nonlinear relations between dependent variable and explanatory variables. PLS and LSSVM are combined together. In PLS-LSSVM model, PLS is used to extract the independent components, then the extracted components is input to the LSSVM with radial basis kernel function for predicting. The LSSVM parameters...
With the appearance of large-scale database and people's increasing concern about individual privacy, privacy-preserving data mining becomes a hot study area, to which the support vector machine(SVM) belongs. In this paper, a novel privacy-preserving SVM for horizontally partitioned data is given. It has comparable accuracy to that of an ordinary SVM as we obtain the SVM by using the distinct property...
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both effective and efficient. Among different kinds of machine learning models, kernel methods are well accepted since they are more robust and accurate than traditional models, such as neural networks. However, learning from...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
User Navigation Behavior Mining (UNBM) mainly studies the problems of extracting the interesting user access patterns from user access sequences (UAS), which are usually used for user access prediction and web page recommendation. Through analyzing the real world web data, we find most of user access sequences carrying hybrid features of different patterns, rather than a single one. Therefore, the...
Accurate rainfall forecasting has been one of the most important role in order to reduce the risk to life and to alleviate economic losses by natural disasters. Recently, support vector regression (SVR) provides an alternative approach for developing rainfall forecasting model due to the use of a risk function consisting of the empirical error and a regularized term which is derived from the structural...
City scientific and technological progress level classification and promotion play a central role in spurring city income growth and reducing poverty. Based on the Chinese city data availability, this paper built evaluation index system on the level of city scientific and technological progress. According to the city scientific and technological progress data which is large scale and imbalance, this...
Innovation system efficiency analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capacity for country. According to the county innovation system data which is large scale and imbalance, this paper presented a support vector machine model to predict county innovation system efficiency. The method was compared with artificial neural...
Talents are the most important resource of high-tech enterprises. Thus conducting an effective early-warning of the brain drain in high-tech enterprises, will effectively reduce the brain drain acts to reduce the loss of high-tech enterprises. This paper, using of high-tech enterprise day-to-day performance appraisal data, in accordance with the characteristics of Chinese high-tech enterprises, carry...
Making precise predictions about the future behavior of a system such as a country's economy, a firm or a lake, or about the population of some species of animal has always been a challenge. While prediction methods and modeling procedures have been developed and used over the past decades, the high degree of uncertainty and complexity that underlie some systems makes it difficult, and in some cases...
The multiclass classification problem has been applied to build a decision function to separate a set of data points into multiple classes. To solve this problem, a number of methods have been developed by extending binary classifications to multiclass classification. However, researches on how to effectively combine multiple hyperplanes to make a decision function are in its early stage. This paper...
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