Abstract
Classification is a process of assigning objects into different classes by their attributes which has been discussed mostly in the field of data mining. There are many classification methodologies in dealing with huge data that apply to various situations and different characteristics of data. Most classification methodologies suggest features selection first to ensure the quality of classification so that the accuracy of classification will not be affected due to redundant or irrelevant features. Diversity classification models will be developed through the reduction of features that improve the accuracy of original classification models. The compound data classification methods are usually employed to establish effective classification models.This research establishes prediction models of classification by data mining methodology. Important attributes are extracted by five various features selection approaches that combine with the four different classifiers to optimize features space. The average accuracy of each approach is compared in combination with different classifiers and nonparametric Wilcoxon signed rank test is taken to show if there is any significant difference between these models. The experimental results demonstrate that the proposed structures outperform original methods and the features selection approach of F-score is a promising method for the fields of data mining.