scholarly journals KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN METODE SUPPORT VECTOR MACHINES (SVM) DAN CLASSIFICATION AND REGRESSION TREES (CART) MENGGUNAKAN GUI R (Studi Kasus di Kabupaten Wonosobo Tahun 2018)

2020 ◽  
Vol 9 (4) ◽  
pp. 525-534
Author(s):  
Lutfia Nuzula ◽  
Alan Prahutama ◽  
Arief Rachman Hakim
Keyword(s):  
Support Vector Machines ◽  
Classification Accuracy ◽  
Regression Trees ◽  
Poverty Line ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Poor People ◽  
Central Java ◽  
Vector Machines ◽  
Classification And Regression

The poor are people who have average monthly expenditures per capita below the poverty line. Wonosobo District became the poorest district in Central Java in 2011-2018, although the percentage of poor people has decreased every year. It cannot be separated from the efforts of the Wonosobo District Government to overcome poverty through various programs. This study classified households in Wonosobo District in 2018 as poor and non-poor based on influencing factors. This study used the Support Vector Machines (SVM) method to be compared with the Classification and Regression Trees (CART) method. It used the data from the 2018 National Socio-Economic Survey of Central Java with a total of 795 observations. Result of the research using the SVM method and the RBF kernel, the classification accuracy reaches 89.82% then the classification accuracy using the CART method reaches 87.08%. GUI designed by RShiny package can make easier for users to analyze the SVM and CART with the valid output. 

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

Support Vector Machines for classification and regression

The Analyst ◽  
2010 ◽  
Vol 135 (2) ◽  
pp. 230-267 ◽  
Author(s):  
Richard G. Brereton ◽  
Gavin R. Lloyd
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Classification And Regression

scholarly journals Tối ưu hóa ước tính mức tiêu thụ năng lượng trong các tòa nhà dựa trên các thuật toán trí tuệ nhân tạo

2020 ◽  
Vol 14 (1V) ◽  
pp. 35-45
Author(s):  
Trần Đức Học ◽  
Lê Tấn Tài
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Ensemble Model ◽  
Chi Square ◽  
Chi Squared ◽  
Classification And Regression ◽  
Automatic Interaction Detector

Mô phỏng và dự báo năng lượng tiêu thụ đóng vai trò quan trọng trong việc thiết lập chính sách năng lượng và đưa ra quyết định theo hướng phát triển bền vững. Nghiên cứu này sử dụng phương pháp kỹ thuật thống kê và công cụ trí tuệ nhân tạo bao gồm mạng nơ-ron thần kinh (ANNs – Artificial neutral networks), máy hỗ trợ véc tơ (SVM – Support vector machine), cây phân loại và hồi quy (CART - Classification and regression trees), hồi quy tuyến tính (LR - Linear regression), hồi quy tuyến tính tổng quát (GENLIN - Generalized linear regression), tự động phát hiện tương tác Chi-squared (CHAID - Chi-square automatic interaction detector) và mô hình tổng hợp (Ensemble model) để dự đoán mức tiêu thụ năng lượng trong các căn hộ tòa nhà chung cư. Bộ dữ liệu để xây dựng mô hình gồm 200 mẫu được khảo sát ở nhiều chung cư tại TP. Hồ Chí Minh. Mô hình đơn có hiệu quả tốt nhất trong quá trình dự đoán là CART, trong khi đó mô hình được tổng hợp tốt nhất là CART + GENLIN. Từ khóa: ước tính; tòa nhà; tiêu thụ năng lượng; khai phá dữ liệu, trí tuệ nhân tạo.

scholarly journals Support Vector Machines Study on English Isolated-Word-Error Classification and Regression

2013 ◽  
Vol 5 (2) ◽  
pp. 531-537
Author(s):  
Abu Bakar Hasan ◽  
Tiong Sieh Kiong ◽  
Johnny Koh Siaw Paw ◽  
Ahmad Kamal Zulkifle
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines ◽  
Classification And Regression ◽  
Isolated Word ◽  
Error Classification

scholarly journals LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS

2017 ◽  
Vol 10 (1) ◽  
pp. 43 ◽  
Author(s):  
Nursuci Putri Husain ◽  
Nursanti Novi Arisa ◽  
Putri Nur Rahayu ◽  
Agus Zainal Arifin ◽  
Darlis Herumurti
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Classification Accuracy ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Support Vector ◽  
Optimal Parameters ◽  
Local Fisher Discriminant Analysis ◽  
Vector Machines ◽  
Improved Ant Colony Algorithm

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.

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