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

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.

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

Expert Systems with Applications ◽

10.1016/j.eswa.2009.06.088 ◽

2010 ◽

Vol 37 (2) ◽

pp. 1535-1541 ◽

Cited By ~ 91

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

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Co-trained support vector machines for large scale unstructured document classification using unlabeled data and syntactic information

Information Processing & Management ◽

10.1016/j.ipm.2003.09.003 ◽

2004 ◽

Vol 40 (3) ◽

pp. 421-439 ◽

Cited By ~ 21

Author(s):

Seong-Bae Park ◽

Byoung-Tak Zhang

Keyword(s):

Support Vector Machines ◽

Large Scale ◽

Document Classification ◽

Unlabeled Data ◽

Support Vector ◽

Vector Machines ◽

Syntactic Information

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Comparison of Support Vector Machines With and Without Latent Semantic Analysis for Document Classification

Data Management, Analytics and Innovation - Advances in Intelligent Systems and Computing ◽

10.1007/978-981-13-1402-5_20 ◽

2018 ◽

pp. 263-274

Author(s):

Vaibhav Khatavkar ◽

Parag Kulkarni

Keyword(s):

Support Vector Machines ◽

Latent Semantic Analysis ◽

Semantic Analysis ◽

Document Classification ◽

Support Vector ◽

Vector Machines

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

The Scientific World JOURNAL ◽

10.1155/2013/419187 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 44

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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Application of Support Vector Machines to the Tracking Control of Piezoelectric Actuators

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.459.82 ◽

2012 ◽

Vol 459 ◽

pp. 82-85

Author(s):

Shu Yan Yang ◽

Hai Feng Wang

Keyword(s):

Support Vector Machines ◽

Tracking Control ◽

Hybrid Approach ◽

Nonlinear Behavior ◽

Piezoelectric Actuators ◽

Experimental Results ◽

Support Vector ◽

Output Displacement ◽

Vector Machines ◽

Final Output

In this paper, a hybrid approach associated the Preisach concept with support vector machines (SVM) is brought forward to identify and predict the nonlinear behavior of piezoelectric actuators (PA). Preisach concept is used to construct mesh nodes in the Preisach plane and determine the final output displacement of PA. SVM is trained based on the mesh nodes and provides favorable generalization ability in the Preisach plane. Experimental results validate the proposed method.

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