scholarly journals Sentiments analysis using Hybrid Approach involving Rule-Based & Support Vector Machines methods

2012 ◽  
Vol 2 (1) ◽  
pp. 55-58 ◽  
Author(s):  
Swati A. Kawathekar
Keyword(s):  
Support Vector Machines ◽  
Hybrid Approach ◽  
Support Vector ◽  
Rule Based ◽  
Vector Machines

A Hybrid Approach to Error Reduction of Support Vector Machines in Document Classification

2006 ◽  
Author(s):  
Y.-S. Tae ◽  
Jeong woo Son ◽  
Mi-hwa Kong ◽  
Jun-Seok Lee ◽  
Seong-Bae Park ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Hybrid Approach ◽  
Error Reduction ◽  
Document Classification ◽  
Support Vector ◽  
Vector Machines

Boolean kernels for rule based interpretation of support vector machines

2019 ◽  
Vol 342 ◽  
pp. 113-124 ◽  
Author(s):  
Mirko Polato ◽  
Fabio Aiolli
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Rule Based ◽  
Vector Machines

A parsimony fuzzy rule-based classifier using axiomatic fuzzy set theory and support vector machines

2011 ◽  
Vol 181 (23) ◽  
pp. 5180-5193 ◽  
Author(s):  
Yan Ren ◽  
Xiaodong Liu ◽  
Jiannong Cao
Keyword(s):  
Support Vector Machines ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Rule ◽  
Support Vector ◽  
Rule Based ◽  
Vector Machines ◽  
Rule Based Classifier ◽  
Axiomatic Fuzzy Set Theory

scholarly journals Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling

2005 ◽  
Vol 54 (7) ◽  
pp. 3009
Author(s):  
Cui Wan-Zhao ◽  
Zhu Chang-Chun ◽  
Bao Wen-Xing ◽  
Liu Jun-Hua
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Fuzzy Rule ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Rule Based ◽  
Vector Machines

scholarly journals Hybridizing support vector machines into genetic algorithm for key factor exploration in core competence evaluation of aviation manufacturing enterprises

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4191-4198
Author(s):  
Linwei Zhai ◽  
Jian Qin ◽  
Lean Yu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machines ◽  
Core Competence ◽  
Hybrid Approach ◽  
Aviation Industry ◽  
Support Vector ◽  
Manufacturing Enterprises ◽  
The Core ◽  
Vector Machines ◽  
Key Factor

In the core competence comprehensive evaluation of aviation manufacturing enterprises, exploring the key factors affecting core competence is crucial to improve the competitiveness of the aviation manufacturing enterprises. In this paper, a novel hybrid approach integrating genetic algorithm (GA) and support vector machines (SVM) is proposed to conduct the key factor exploration tasks in the core competitiveness evaluation of aviation manufacturing enterprises. In the proposed hybrid GA-SVM approach, the GA is used for key factor exploration, while SVM is used to calculate the fitness function of the GA method. Using the survey data from Aviation Industry Corporation of China (AVIC), some experiments analysis is conducted to test the effectiveness of the proposed hybrid approach. Empirical results demonstrate that the proposed hybrid GA-SVM approach can be used as an alternative solution to key factor exploration.

A hybrid approach of DEA, rough set and support vector machines for business failure prediction

2010 ◽  
Vol 37 (2) ◽  
pp. 1535-1541 ◽  
Author(s):  
Ching-Chiang Yeh ◽  
Der-Jang Chi ◽  
Ming-Fu Hsu
Keyword(s):  
Support Vector Machines ◽  
Rough Set ◽  
Hybrid Approach ◽  
Failure Prediction ◽  
Business Failure ◽  
Support Vector ◽  
Vector Machines

scholarly journals Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mustafa Serter Uzer ◽  
Nihat Yilmaz ◽  
Onur Inan
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Artificial Bee Colony ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Bee Colony ◽  
Vector Machines

This paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

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