Credit Rating Analysis with Support Vector Machines and Artificial Bee Colony Algorithm

2013 ◽  
pp. 528-534 ◽  
Author(s):  
Mu-Yen Chen ◽  
Chia-Chen Chen ◽  
Jia-Yu Liu
Keyword(s):  
Support Vector Machines ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Credit Rating ◽  
Support Vector ◽  
Bee Colony ◽  
Vector Machines

An Enhanced Artificial Bee Colony Optimizer for Predictive Analysis of Heating Oil Prices using Least Squares Support Vector Machines

2014 ◽  
pp. 149-173
Author(s):  
Zuriani Mustaffa ◽  
Yuhanis Yusof ◽  
Siti Sakira Kamaruddin
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Artificial Bee Colony ◽  
Human Life ◽  
Oil Prices ◽  
Predictive Analysis ◽  
Support Vector ◽  
Bee Colony ◽  
Vector Machines ◽  
Heating Oil

As energy fuels play a significant role in many parts of human life, it is of great importance to have an effective price predictive analysis. In this chapter, the hybridization of Least Squares Support Vector Machines (LSSVM) with an enhanced Artificial Bee Colony (eABC) is proposed to meet the challenge. The eABC, which serves as an optimization tool for LSSVM, is enhanced by two types of mutations, namely the Levy mutation and the conventional mutation. The Levy mutation is introduced to keep the model from falling into local minimum while the conventional mutation prevents the model from over-fitting and/or under-fitting during learning. Later, the predictive analysis is followed by the LSSVM. Realized in predictive analysis of heating oil prices, the empirical findings not only manifest the superiority of eABC-LSSVM in prediction accuracy but also poses an advantage to escape from premature convergence.

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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