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

Article Type:
Research/Original Article (بدون رتبه معتبر)

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

Journal of Innovations of Aplied Information and Communication Technology, Volume:1 Issue: 4, 2023
15 to 32  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 1,390,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!