Providing a Combination Classification (Honeybee Clooney and Decision Tree) Based on Developmental Learning - Ahad Zolfagharifar, Seyed; Karamizadeh, Faramarz; Parvin, Hamid (Modern applied science, 2015)
The aim of this study is to provide a combination classification based on developmental learning in the proposed method using algorithms inspired by nature (honeybee Clooney) and decision tree, by using algorithm classifier consensus is proposed that this method, at first classifier once implemented and based on the detection rate of input data agreement in the final consensus which is an innovation in this research. To implement the proposed algorithms used MATLAB software. Note that, this is an increase compared to the classifiers ensemble, it have accuracy and fix. This shows that this method of making the ensemble by helping bee Clooney algorithm, when appropriate and effective which the number of data collection records is high or the number of study characteristics is high. In this study, we proposed algorithm on 8 samples tested. However, training time of this method compared with simple ensemble is a slower process but this method compared with simple ensemble method has higher accuracy, this shows, if we want a higher accuracy, we should be spent more time. In general, if the accuracy of the process have a large importance for us, this method can be a good option to get the results that almost optimal.