جستجوی مقالات مرتبط با کلیدواژه « Datamining » در نشریات گروه « علوم انسانی »

The present study examines the usefulness of  Relief method and data mining in predicting tax evasion of listed companies in Tehran Stock Exchange (TSE) using accounting data and decision tree patterns in two situations: with and without the phase of selecting variables. The statistical population of this study includes all companies accepted in TSE from 2005 to 2015, and the research sample included 1.081 company-years. One-way ANOVA, independent sample t-test, decision tree algorithms, and the Relief method of selecting variables were used for data analysis. Data was analyzed using SPSS and Weka softwares. The results of  Relief algorithm showed that ratio of operating profit to total assets, ratio of return on assets, and market value of company are more appropriate variables than other variables for predicting tax evasion. In addition, the results of one-way ANOVA showed that the difference in prediction accuracy of different decision tree methods is statistically significant. However, when each of these algorithms compared with other separately, both states of with and without the phase of selecting optimal variables, the results showed that only LMT algorithms results were significantly different with each other. In other algorithms, even though the results improved but this was not statistically significant. In other words, using relief method does not improve the results in all algorithms.

Financial ratios have been one of the strongest sources to evaluate the financial performance of Tehran Stock Exchange companies. One of the methods used to predict the performance is data mining algorithm. In this study, four decision tree model used to compare evaluate the performance, implementation and evaluation criteria with models. A sample of 21 of the 534 companies listed on Tehran Stock Exchange between 1390 to 1393 as the independent variables and ratios return on assets and return on equity has been selected as dependent variables. Among models, C5.0 decision tree model in all methods has higher accuracy in predicting the performance was evaluated.