Analyzing foreign exchange rates by rough set theory and directed acyclic graph support vector machines

2010 ◽  
Vol 37 (8) ◽  
pp. 5993-5998 ◽  
Author(s):  
Ping-Feng Pai ◽  
Shi-Yu Chen ◽  
Chao-Wei Huang ◽  
Ya-Hsin Chang
Keyword(s):  
Support Vector Machines ◽  
Exchange Rates ◽  
Set Theory ◽  
Rough Set ◽  
Directed Acyclic Graph ◽  
Rough Set Theory ◽  
Support Vector ◽  
Foreign Exchange Rates ◽  
Acyclic Graph ◽  
Vector Machines

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals Boosting Accuracy of Classical Machine Learning Antispam Classifiers in Real Scenarios by Applying Rough Set Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
N. Pérez-Díaz ◽  
D. Ruano-Ordás ◽  
F. Fdez-Riverola ◽  
J. R. Méndez
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Wide Range ◽  
Vector Machines ◽  
Bayes Algorithm

Nowadays, spam deliveries represent a major problem to benefit from the wide range of Internet-based communication forms. Despite the existence of different well-known intelligent techniques for fighting spam, only some specific implementations of Naïve Bayes algorithm are finally used in real environments for performance reasons. As long as some of these algorithms suffer from a large number of false positive errors, in this work we propose a rough set postprocessing approach able to significantly improve their accuracy. In order to demonstrate the advantages of the proposed method, we carried out a straightforward study based on a publicly available standard corpus (SpamAssassin), which compares the performance of previously successful well-known antispam classifiers (i.e., Support Vector Machines, AdaBoost, Flexible Bayes, and Naïve Bayes) with and without the application of our developed technique. Results clearly evidence the suitability of our rough set postprocessing approach for increasing the accuracy of previous successful antispam classifiers when working in real scenarios.

scholarly journals Jumlah Transisi pada Ciri Transisi dalam Pengenalan Pola Tulisan Tangan Aksara Jawa Nglegeno dengan Multiclass Support Vector Machines

2012 ◽  
Vol 8 (1) ◽  
pp. 18
Author(s):  
Azis Wisnu Widhi Nugraha ◽  
Widhiatmoko Hery Purnomo
Keyword(s):  
Support Vector Machines ◽  
Directed Acyclic Graph ◽  
Recognition System ◽  
Support Vector ◽  
Paper Report ◽  
Acyclic Graph ◽  
System Transition ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Multiclass Support Vector Machines

<p>Feature extraction is one of the most improtant step on characters recognition system. Transition features is one from many features used on characters recognition system. This paper report a research on handwritten basic Jawanesse characters recognition system to found the proper numbers of transitions used on transition features. To recognize the characters,the Multiclass Support Vector Machines were used. The Directed Acyclic Graph (DAG) SVM were used for multiclass classification strategy and to map each input vector to a higher dimention space, the Gaussian Radial Basis Function (RBF) kernel with parameter 1were used. It can be shown, for basicJawanesse characters recognition system, the optimal numbers of transitions used for transition features is 4 (a half of maximum numbers of transition on all patterns).</p>

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