جستجوی مقالات مرتبط با کلیدواژه "classification" در نشریات گروه "آمار"

Cancer progression among patients can be assessed by creating a set of gene markers using statistical data analysis methods. Still, one of the main problems in the statistical study of this type of data is the large number of genes versus a small number of samples. Therefore, it is essential to use dimensionality reduction techniques to eliminate and find the optimal number of genes to predict the desired classes accurately. On the other hand, choosing an appropriate method can help extract valuable information and improve the machine learning model's efficiency. This article uses an ensemble learning approach, a random support vector machine cluster, to find the optimal feature set. In the current paper and in dealing with real data, it is shown that via randomly projecting the original high-dimensional feature space onto multiple lower-dimensional feature subspaces and combining support vector machine classifiers, not only the essential genes are found in causing prostate cancer, but also the classification precision is increased.

‎This paper presents a nonparametric multi-class depth-based classification approach for multivariate data. This approach is easy to implement rather than most existing nonparametric methods that have computational complexity. If the assumption of the elliptical symmetry holds, this method is equivalent to the Bayes optimal rule. Some simulated data sets as well as real example have been used to evaluate the performance of these depth-based classifiers.

Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram.

This article introduces a semiparametric multinomial logistic regression model to classify labeled configurations. In the regression model, the explanatory variable is the kernel function obtained using the power-divergence criterion. Also, the response variable was categorical and showed the class of each configuration. This semiparametric regression model is introduced based on distances defined in the shape space, and for this reason, the correct classification of shapes using this method has been improved compared to previous methods. ‎The performance of this model has been investigated in the comprehensive simulation study‎. ‎Two real datasets were analyzed using this article's method as an application‎. ‎Finally‎, ‎the method presented in this article was compared with the techniques introduced in the literature‎, ‎which shows the proper performance of this method in classifying configurations‎.

The Internet of Things is suggested as the upcoming revolution in the Information and communication technology due to its very high capability of making various businesses and industries more productive and efficient. This productivity comes from the emergence of innovation and the introduction of new capabilities for businesses. Different industries have shown varying reactions to IOT, but what is clear is that IOT has applications in all Businesses. These applications have made significant progress in some industries such as health and transportation but is under development in others, namely agriculture and animal husbandry. In fact, the production of data bases on the Internet of Things is one of the main pillars in the field of big data and data science, Therefore, statistical concepts and models that are used in data science can be beneficially implemented in such data. Among the valid statistical models, Bayesian statistics for data is being utilized in these studies. In this research the fundamentals of Bayesian statistics for big data and most notably the data produced by IOT is explained. They have been Pragmatically examined in both road traffic as well as people’s social behavior towards using vehicles, which have had practically and scientifically valid results.

Nonrespose is a source of error in the survey results and national statistical organizations are always looking for ways to control and reduce it. Predicting nonrespons sampling units in the survey before conducting the survey is one of the solutions that can help a lot in reducing and treating the survey nonresponse. Recent advances in technology and the facilitation of complex calculations have made it possible to apply statistical learning methods, such as regression and classification trees or support vector machines, to many issues, including predicting the nonresponse of sampling units in statistics. In this article, while reviewing the above methods, the nonresponse sampling units are predicted using them in an establishment survey and it is shown that a combination of the above methods is more accurate in predicting the correct nonresponse than any of these methods.

The Area under the ROC Curve (AUC) is a common index for evaluating the ability of the biomarkers for classification. In practice, a single biomarker has limited classification ability, so to improve the classification performance, we are interested in combining biomarkers linearly and nonlinearly. In this study, while introducing various types of loss functions, the Ramp AUC method and some of its features are introduced as a statistical model based on the AUC index. The aim of this method is to combine biomarkers in a linear or non-linear manner to improve the classification performance of the biomarkers and minimize the experimental loss function by using the Ramp AUC loss function. As an applicable example, in this study, the data of 378 diabetic patients referred to Ardabil and Tabriz Diabetes Centers in 1393-1394 have been used. RAUC method was fitted to classify diabetic patients in terms of functional limitation, based on the demographic and clinical biomarkers. Validation of the model was assessed using the training and test method. The results in the test dataset showed that the area under the RAUC curve for classification of the patients according to the functional limitation, based on the linear kernel pf biomarkers was 0.81 and with a kernel of the radial base function (RBF) was equal to 1.00. The results indicate a strong nonlinear pattern in the data so that the nonlinear combination of the biomarkers had higher classification performance than the linear combination.

Despite the physicians and nutritionists recommendations to consume the fruits and vegetables, they point much less on how to choose fruits and vegetables in daily meals. In this study, with a novel approach, fruits and vegetables have been classified on the basis of their similar characteristics and the amount of certain nutrients. One of the goals of diet is the diversity and balance of nutrition; therefore the classification of fruits and vegetables on the basis of their nutritional value makes balancing the nutritional intake of these important food groups and also finding a suitable replacement for them feasible. In order to meet the nutritional needs, using principal component analysis and cluster analysis, fruits and vegetables are classified into several groups. MATLAB software was used for doing this classification.

Decision Tree is one of the widely used data classification techniques. This paper proposes uncertain decision tree classification method. Lots of Factors causes Value uncertainty including measurements precision limitation, outdated sources, lack of information, and transmission problems. With uncertainty, the value of a data item is often represented not only by one single value, but also by multiple values forming a probability distribution.Data of family economical information plan survey are uncertain because of reticence and lack of data. We need to have appropriate algorithm to work with uncertain data with satisfactory accuracy. In this paper, we upgrade the traditional uncertain decision tree algorithm, using entropy and information gain, and extend measures, including the uncertain data interval and probability distribution function which help in reducing the demanding effects of imbalance data on the output of algorithm. Our algorithm can handle both certain and uncertain datasets. This paper indicates that, the proposed algorithm has satisfactory prediction accuracy.Uncertain Decision tree construction on data use much more CPU than that for certain data. To tackle this problem, we propose a max level technique that can greatly improve construction efficiency.

Logistic regression models in classification problems by assuming the linear effects of covariates is a modeling for class membership posterior probabilities. The main problem that includes nonlinear combinations of covariates is maximum likelihood estimation (MLE) of the model parameters. In recent investigations، an approach of solving this problem is combination of neural networks، evolutionary algorithms and MLE methods. In this paper، another type of radial basis functions، namely inverse multiquadratic functions and hybrid method، are considered for estimating the parameters of these models. The experimental results of comparing the proposed models show that the inverse multiquadratic functions compared to the Gaussian functions have better precision in classification problems.

In recent years, we have been faced with increasing growing use of data mining and database and parallel to this, new science and technologies have been developed to make the best of these huge volumes of data in customer relationship management, optimized use of data is a competitive advance for companies and organization. And if so, they can deserve their old customers and acquire new ones. In this regard, the banking industry is one of those industries that deal with huge volume data of the customers. In this article, we have considered customers churn in Mehr finance and credit institution. In the first step of this thesis, the literature review results is reported and based on the review, different aspects of problem properties is investigated and then, the required data is gathered. The churn modeling of data is performed in a hybrid form: first, we have applied fuzzy modeling for classifying customers in 3 categories as Active, Moderate and weak. Then, we have made use of support vector machine for churn modeling. Numerical results of the problem model are compared with logical approach. Finally, based on these numerical results, we can conclude that the hybrid Fuzzy – SVM approach is a more exact and useful approach for churn prediction.

Statistics and information are the basic requirements of scientific development in any domain. To have an accurate image of leisure and cultural domain, statistics and information, which are up-to-date and adapted to each countries realities, are also needed. Observing the standards of producing the statistics in cultural domain provides conditions that by virtue of them, one can draw up the macro-planning and micro-planning of society in the domain of art and culture more precisely and more accurately. It is done by using a general classification of activities, products and cultural occupations as a framework of gathering information and statistics. The existence of such an integrated classification results in promotion of integration of statistical information and deceasing of duplication and parallel activities. By establishment of the SCI and according to part? ?? of article 3 of the low of SCI, this center is responsible for preparing of definitions, assigning the concepts, criterion and statistical classification and according to article 8 of the same law, all of the executive organizations are obliged to obey them. This article is presented with the aim of executing the relevant laws. According to compiling Iran culture and leisure classification, it also examines the definitions, concepts and comparative studies, and finally draws up the classification in 3 distinct parts as cultural activities, cultural products and cultural occupations.