Data Optimization with Multilayer Perceptron Neural Network and Using New Pattern in Decision Tree Comparatively

Abstract

Problem statement: The aim of the present study is to exemplify the use of Artificial Neural Networks (ANN) for parameter prediction. Missing value or unreal approach to some questions in scale is a problem for unbiased findings. To learn a real pattern with ANN provides robust and unbiased parameter estimation. Approach: To this end, data was collected from 906 students using “Scale of student views about the expected situations and the current expectations from their families during learning process” for the study entitled “Student views about the expected situations and the current expectations from their families during learning process”. In the study, first the initial data set gathered using the measurement tool and the new data set produced by Multi-Layer Receptors algorithm, which was considered as the highest predictive level of ANN for the research were individually analyzed by Chaid analysis and the results of the two analyses were compared. Results: The findings showed that as a result of Chaid analysis with the initial data set the variable “education level of mother” had a considerable effect on total score dependent variable, while “education level of father” was the influential variable on the attitude level in the data set predicted by ANN, unlike the previous model. Conclusion/Recommendations: The findings of the research show Artificial Neural Networks could be used for parameter estimation in cause-effect based studies. It is also thought the research will contribute to extensive use of advanced statistical methods.