Browsing by Author "Kibekbaev, Azamat"
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ArticlePublication Metadata only Benchmarking regression algorithms for income prediction modeling(Elsevier, 2016) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, AzamatThis paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.Conference paperPublication Metadata only Benchmarking regression algorithms for income prediction modeling(IEEE, 2015) Kibekbaev, Azamat; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kibekbaev, AzamatThis paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.Conference paperPublication Metadata only Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection(World Academy of Science, Engineering and Technology, 2015) Zakaryazad, Ashkan; Duman, Ekrem; Kibekbaev, Azamat; Industrial Engineering; DUMAN, Ekrem; Zakaryazad, Ashkan; Kibekbaev, AzamatA typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.