An empirical study of the performance of two stage optimal ensemble classification using genetic algorithm

5th International Seminar on Sciences

自然科学（总论）

Raharjo, R.D.R.^1 ; Soleh, A.M.^1 ; Sartono, B.^1

Department of Statistics, IPB University, Bogor

16680, Indonesia^1

关键词: Classification methods;    Comprehensive evaluation;    Empirical studies;    Ensemble modeling;    K nearest neighbours (k-NN);    Logistic regressions;    Optimal ensemble;    Predictor variables;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/299/1/012024/pdf
DOI  :  10.1088/1755-1315/299/1/012024

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

Zelenkov et al. [1] proposed a two-step classification method (TSCM) based on genetic algorithm to predict the bankruptcy of Russian companies. This current study was conducted to do more comprehensive evaluation than Zelenkov et al. [1] did. We involved more datasets, compared to a greater number of competitive methods, and developed the ensemble using more base classifiers. The datasets consisted of nine datasets and then the result of prediction compared to previous studies from 13 papers that were published between 1996 and 2009. In addition, the previous method using five base classifiers, this study involved seven base classifiers. The purpose of this study is to compare the accuracy of prediction from previous studies with this method. In this study, we used k-Nearest Neighbour (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Decision Tree (DT), support vector machine (SVM), random forest (RF), and boosting as the base classifiers of ensemble model. Genetic algorithm is used to find the best predictor variables and the best weight for each base classifier. The result of TSCM shows that the accuracy of prediction can increase about 0-34% for some datasets compared to the previous studies.