Journal of Computer Science 6 (5): 606-612, 2010 ISSN 1549-3636 © 2010 Science Publications Corresponding Author: Murat Kayri, Department of Computer Science, Yuzuncu Yıl University, Faculty of Education, Van/Turkey. Most of this research was orally presented in the 2nd National Congress on Measurement and Evaluation in Education and Psychology 606 Data Optimization with Multilayer Perceptron Neural Network and Using New Pattern in Decision Tree Comparatively 1 Murat Kayri and 2 Omay Cokluk 1 Department of Computer Science, Yuzuncu Yıl University, Faculty of Education, Van/Turkey 2 Department of Measurement and Evaluation, Ankara University, Faculty of Education, Ankara/Turkey 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. Key words: Artificial neural networks, multi-layer perceptron, Chaid analysis, back-propagation INTRODUCTION Artificial Neural Networks (ANN) is an artificial intelligence application developed from the neural (neurological) pattern of human brain’s learning spot. First studies on ANN started with modeling the nerve cells forming the brain and the application of these modeling’s into the computer systems (Haykin,1999). Afterwards, it has become common in many fields in parallel with the development in computer systems. ANN has efficient usage in many fields such as medicine, industry, biology, electronic systems, optimization and social sciences (Golden, 1996). Artificial Neural Networks could conduct both linear and non-linear model approaches together and so it can get the correlation between variables on a more valid basis (Erilli et al., 2010). ANN is accepted as a strong method that learns the structures of the current data, establishes a new relations network in the real world and conducts many statistical processes such as making parameter estimation, classification, optimization and time series in this relations network in a determined way (Badr et al., 2003; Elmas, 2003; Fausett, 1994; Uzun and Erdem, 2005). This method analyzes the data set in three stages. At the first stage, a considerable portion of the data set is used for “training process”. ANN tries to detect the relationships between the variables of the data set and so it tries to determine the characteristic of the research pattern. At the second stage, based on the learnings of the first stage, it tries to perceive the model and this process is named the “perceptron process/hidden process”. In the perceptron process, the ideal functions that belong to the model are produced and Weights (W i ) of explanatory variable(s) upon the dependent variables are obtained. The third stage is the new model estimation the ANN produces for the real world and this process is named the “output process” (Manel et al., 1999). The first artificial neural network model was developed in 1943 by Warren McCulloch, a neurologist