Providing a solution for predicting tax fraud companies based on optimized decision tree, support vector machine and Bayesian network

Many companies disrupt the economic system by violating their financial statements, which has led to a major economic crisis. There are various diagnostic strategies that are mostly human. These solutions have high costs for calculating and reviewing the financial statements of all companies, so we must look for a solution that can use data mining and automation to perform this diagnostic process. Of course, data mining methods have also been proposed for this case, each of which has advantages and disadvantages. The data mining methods presented so far have high computational overhead or low accuracy. However, in the method proposed in this research, the improved ID3 decision tree with Bayesian network and support vector machine has been used as a combined method. In this proposed method, to improve performance and accuracy, the rough set algorithm and hierarchical analysis are used to select effective features. The tree created in the proposed method has the lowest possible depth and therefore has a high velocity. The computational overhead of the proposed method is low due to the use of an optimal algorithm. The data used in the evaluation of 3-year data from 60 companies. In evaluating the proposed method, it is shown that the proposed method has an accuracy of 80%, which is considered to be high accuracy compared to similar methods. The time overhead in the proposed method is O(m.n) and the memory overhead is O(n) where m represents the size of the training set and n represents the feature set used in the training