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Showing 4 results for Classification
Arezou Mojiri, Soroush Alimoradi, Mohammadreza Ahmadzade,
Volume 7, Issue 1 (9-2013)
Abstract
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.
Meisam Moghimbeygi,
Volume 16, Issue 2 (3-2023)
Abstract
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.
Miss Nilia Mosavi, Dr. Mousa Golalizadeh,
Volume 17, Issue 2 (2-2024)
Abstract
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.
Sara Bayat, Sakineh Dehghan,
Volume 17, Issue 2 (2-2024)
Abstract
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.
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