PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA

To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate. Keywords: fixed effect model, exponential adaptive kernel

A Support Vector Classifier Based on Vague Similarity Measure

Support vector machine (SVM) is a popular machine learning method for its high generalizaiton ability. How to find the adaptive kernel function is a key problem to SVM from theory to practical applications. This paper proposes a support vector classifer based on vague sigmoid kernel and its similarity measure. The proposed method uses the characteristic of vague set, and replaces the traditional inner product with vague similarity measure between training samples. The experimental results show that the proposed method can reduce the CPU time and maintain the classification accuracy

A novel online LS-SVM approach for regression and classification

In this paper, a novel online least squares support vector machine approach is proposed for classification and regression problems. Gaussian kernel function is used due to its strong generalization capability. The contribution of the paper is twofold. As the first novelty, all parameters of the SVM including the kernel width parameter σ are trained simultaneously when a new sample arrives. Unscented Kalman filter is adopted to train the parameters since it avoids the sub-optimal solutions caused by linearization in contrast to extended Kalman filter. The second novelty is the variable size moving window by an intelligent update strategy for the support vector set. This provides that SVM model captures the dynamics of data quickly while not letting it become clumsy due to the big amount of useless or out-of-date support vector data. Simultaneous training of the kernel parameter by unscented Kalman filter and intelligent update of support vector set provide significant performance using small amount of support vector data for both classification and system identification application results. © 201

Data-Adaptive Kernel Support Vector Machine

In this thesis, we propose the data-adaptive kernel Support Vector Machine (SVM), a new method with a data-driven scaling kernel function based on real data sets. This two-stage approach of kernel function scaling can enhance the accuracy of a support vector machine, especially when the data are imbalanced. Followed by the standard SVM procedure in the first stage, the proposed method locally adapts the kernel function to data locations based on the skewness of the class outcomes. In the second stage, the decision rule is constructed with the data-adaptive kernel function and is used as the classifier. This process enlarges the magnification effect directly on the Riemannian manifold within the feature space rather than the input space. The proposed data-adaptive kernel SVM technique is applied in the binary classification, and is extended to the multi-class situations when imbalance is a main concern. We conduct extensive simulation studies to assess the performance of the proposed methods, and the prostate cancer image study is employed as an illustration. The data-adaptive kernel is further applied in feature selection process. We propose the data-adaptive kernel-penalized SVM, a new method of simultaneous feature selection and classification by penalizing data-adaptive kernels in SVMs. Instead of penalizing the standard cost function of SVMs in the usual way, the penalty will be directly added to the dual objective function that contains the data-adaptive kernel. Classification results with sparse features selected can be obtained simultaneously. Different penalty terms in the data-adaptive kernel-penalized SVM will be compared. The oracle property of the estimator is examined. We conduct extensive simulation studies to assess the performance of all the proposed methods, and employ the method on a breast cancer data set as an illustration. The data-adaptive kernel is further applied in feature selection process. We propose the data-adaptive kernel-penalized SVM, a new method of simultaneous feature selection and classification by penalizing data-adaptive kernels in SVMs. Instead of penalizing the standard cost function of SVMs in the usual way, the penalty will be directly added to the dual objective function that contains the data-adaptive kernel. Classification results with sparse features selected can be obtained simultaneously. Different penalty terms in the data-adaptive kernel-penalized SVM will be compared. The oracle property of the estimator is examined. We conduct extensive simulation studies to assess the performance of all the proposed methods, and employ the method on a breast cancer data set as an illustration