Feature Selection for Financial Data Classification: Islamic Finance Application

The rapid growth of computing technology to process vast amount of data has impelled more interest in data mining. Such interest was mainly aimed at knowledge discovery to improve decision making process in diverse range of applications, including Islamic finance. One of the most critical steps in data mining is data preprocessing, as it would directly affect the quality of insights obtained at the later stage. Feature selection has been widely used in data preprocessing phase to improve the machine-learning algorithm and model interpretability. However, there has been limited attention has been given on the evaluation of feature selection methods on its effectiveness to process input data for Induction Decision Tree (IDT). Hence, this study aims to address such gap in the literature through the use of real-world data in Islamic finance to evaluate the improvement that generated by feature selection method. The result of the study shows that the use of such technique has resulted in better performance of the IDT model generated in the study.

[1]  Andreas Holzinger,et al.  Data Mining with Decision Trees: Theory and Applications , 2015, Online Inf. Rev..

[2]  Edward I. Altman,et al.  FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY , 1968 .

[3]  Huan Liu,et al.  Feature selection for classification: A review , 2014 .

[4]  Robert C. Holte,et al.  Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria , 2000, ICML.

[5]  Jae Kyeong Kim,et al.  A literature review and classification of recommender systems research , 2012, Expert Syst. Appl..

[6]  Ingoo Han,et al.  A case-based approach using inductive indexing for corporate bond rating , 2001, Decis. Support Syst..

[7]  Amir Arzy Soltan,et al.  A hybrid model using decision tree and neural network for credit scoring problem , 2012 .

[8]  Mira Kartiwi,et al.  The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences , 2015 .

[9]  Rodney Wilson.  Innovation in the structuring of Islamic sukuk securities , 2008 .

[10]  Kuldeep Kumar,et al.  Business failure prediction using decision trees , 2010 .

[11]  Martina Novotná.  The Use of Different Approaches for Credit Rating Prediction and Their Comparison , 2012 .

[12]  Kee S. Kim,et al.  Predicting bond ratings using publicly available information , 2005, Expert Syst. Appl..

[13]  Petr Hájek,et al.  Credit rating modelling by kernel-based approaches with supervised and semi-supervised learning , 2011, Neural Computing and Applications.

[14]  Teddy Surya Gunawan,et al.  Decision tree optimization for sukuk rating prediction , 2018 .