a preprocessing technique to investigate the stability of multiobjective heuristic ensemble classifiers

Background and objectives: according to the random nature of heuristic algorithms, stability analysis of heuristic ensemble classifiers has particular importance. methods: the novelty of this paper is using a statistical method consists of plackett-burman design, and taguchi for the first time to specify not only important parameters, but also optimal levels for them. minitab and design expert software programs are utilized to achieve the stability goals of this research. results: the proposed approach is useful as a preprocessing method before employing heuristic ensemble classifiers; i.e., first discover optimal levels of important parameters and then apply these parameters to heuristic ensemble classifiers to attain the best results. another significant difference between this research and previous works related to stability analysis is the definition of the response variable; an average of three criteria of the pareto front is used as response variable. finally, to clarify the performance of this method, obtained optimal levels are applied to a typical multi-objective heuristic ensemble classifier, and its results are compared with the results of using empirical values; obtained results indicate improvements in the proposed method. conclusion: this approach can analyze more parameters with less computational costs in comparison with previous works. this capability is one of the advantages of the proposed method.