scholarly journals Optimization of Hyperparameters and Feature Selection for Random Forests and Support Vector Machines by Artificial Bee Colony Algorithm

2019 ◽  
Vol 34 (2) ◽  
pp. G-I36_1-11
Author(s):  
Hisashi Kondo ◽  
Yuma Asanuma
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Random Forests ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Support Vector ◽  
Bee Colony ◽  
Vector Machines ◽  
Selection For

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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