ARAB ACADEMY FOR SCIENCE, TECHNOLOGY AND MARITIME TRANSPORT

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ARAB ACADEMY FOR SCIENCE, TECHNOLOGY AND MARITIME TRANSPORT

(AASTMT)

College of Computing and Information Technology Department of Information Systems

Feature Selection and Classification in Corporate Banking Lending Systems

By

Y ASMIN GAMAL IBRAHIM F AHMY

A Thesis Submitted to the College of Computing and Information Technology, AASTMT as Partial

Fulfdlment of the Requirements for the Award of the Degree of

Prof. Dr. Ramadan Moawad Computer Science

Department Head,

College of Computing and Information Technology AASTMT

MASTER OF SCIENCE

In

INFORMATION SYSTEMS

Supervisors:

Prof. Dr. Mohamed Waleed Fakhr

Dean of College of

Computing and Information Technology

AASTMT

October 20 to

Prof. Dr. Amr Badr Professor of Computer Science,

Faculty of Computers and Information

Cairo University

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Arab Academy for Science and Tf'chnology and Maritime Transport DECLARATION

We clarify that we have read the present work and that in our opinion it is fully adequate in scope and quality as dissertation towards the partial fulfillment of th., Master Degree requirements in

Specialization: Information System From

College of Computing and Information Technology (AASTMT)

Date October 4 - 2010 Thesis Title

"FEATURE SELECTION AND CLASSIFICATION IN CORPORATE BANKING LENDING SYSTEMS"

Submitted By

Yasmin Gamal Ibrahim Fahmy

SupeNisor (s):

Name: Prof. Dr. Ramadan Moawad

Position: Head of Computer Science, College of Computing and Information

Technology, Arab Academy for Science and Technology and MaritimeTransport Signature:... .... . ...•... ~ ... ;:;. .. _ _ _ _

Name: Prof. Dr. Mohamed Waleed Fakhr

Position: Dean of College of Computing and Information

Technology, Arab Academy for Science and Technology and Maritime Transport signature: ...

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Name: Prof. Dr. Amr Badr

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lX 2033 AI-Horia - Heliopolis - Cairo - A.R.E. - Tel.: 268!,G15 - 2685616 - 2685617 - Fax: 685840

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Arab Academy for Scllncl and T,(:hnology and Maritime Transport

EI.mllleds) :

Name: Prof. Dr. Abdel Badeeh Salem

Position: Professor of Computer Science and Information Systems - Ain Shams University

Name: Prof. Dr. Ibrahim Farag

Position: Professor of Computer Science and Information Systems - Cairo University

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)X 2033 AI-Horia· Heliopolis· Cairo· A.R.E.· Tel.: 268!;I)15· 2685616·2685617· Fax: 6858.10

Certification:

I certify that all the material in this thesis that is not my own work has been identified, and that no material is included for which a degree has previously been conferred on me.

The contents of this thesis reflect my own personal views, and are not necessarily endorsed by the Academy.

Student name: Yasrnin Gamal Ibrahim Fahmy

Signature:

I

Published Work:

The work in the thesis is published in one paper as follows:

1. Yasmin Gamal Fahmy, Ramadan Moawad, Amr Badr, Waleed Fakhr, "A Feature Selection and Classification Tool for Corporate Banking Lending Process", Egyptian Computer Science Journal Vol. 34 No. 1 January 2010

11

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(ff) ~]l

III

Acknowledgment

First of all, I wish to express my gratitude to Allah whose great help is the first in everything I can do in life. Then, I am very grateful to the people who helped me during the thesis and provided me with the suitable environment in which I could work and be productive. I want to express my deepest thanks to my family for their praying for me, trust, love and advice. The present thesis would have never been done without their help and support. My parent's encouragement was the greatest motive behind any good step I have ever taken in my life.

I sincerely want to express my deep feeling of thanks to my supervisor Dr. Ramadan Moawad for his help, useful suggestions, and directions for this thesis. I would also like to thank

Waleed Fakhr for support and knowledge. His scientific way of thinking was a real guide to me in all steps. I would like to show my sincerest gratitude to Dr. Amr Badr who was more of a father to me during my study where every discussion with him helped me through out all the points and goals.

Special thanks go to Dr. Abdel Fattah Hegazy whose supervision during the whole master preparation phase was of great guidance and support.

Last, but not least, special thanks go to all AASTMT Drs. and staff who have raised me and supported me through the education phase since

2002 and thanks to anyone who helped me by any improvements, recommendations, ideas, or even a word ...

IV

Abstract

Corporate Credit Risk Analysis is one of the most critical activities in the banking domain. This Analysis is essential prior to any decision making regarding granting any financial facilities to corporate customers. Currently Corporate Credit Risk Analysis mainly depends on judgmental methods, where granting financial facilities to a corporate customer is mainly subject to the credit analyst's judgment and analysis performed on the corporate information. The analysis performed by the credit risk analyst considers all the corporate information which is composed of huge number of quantitative and qualitative factors. Therefore, the contribution of feature selection and classification techniques in such a complicated process is becoming very important to increase the decision accuracy, facilitate the decision maker's daily work, provide decision support powerful information, help reduce operational time and effort and eliminate human error.

This thesis presents a background on currently used methods for credit risk analysis, a study on feature selection methods, a study on classification methods. The thesis also presents solutions for the problem using feature selection methods and classification.

Classification helps automate the decision making using supervised machine learning teChniques and after using training data to train the system and allow it learn the decision rules and start predicting test data decision with high accuracy. Feature Selection helps considering the most effective features affecting the decision before being introduced to the classification mechanism. Feature selection has contributed a lot in solving the problem by reducing computation time and eliminating redundant features.

v

Different feature selection algorithms were applied prior to classification like Principle Component analysis (PCA) and Support Vector Machine (SVM) as a Feature Selector achieving good contribution in achieving better classification results. The use of Genetic Algorithm feature selection technique combined with the Support Vector Machine objective function for evaluation (GA - SVM) was proposed as the best problem solution achieving best classification accuracy results with the most optimized features set considered in the decision.

VI

Table of Contents

Certification: ... I Published Work: ... II Acknowledgment ... IV Abstract ... V Table of Contents ... VII List of Figures: ... IX List of Tables ... XI List of Symbols and Abbreviations ... XII

Chapter One: ... , '" ... '" ... 1

Introduction " ... 1

1.1 Background and Problem Definition ... 2

1.2 Thesis Objectives ... 7

1.3 Thesis Scope of Work ... 7

1.4 Thesis Organization ... 8

Chapter Two: ... 9

Study on Credit Risk Analysis Methods ... 9

2.1 Introduction to currently used methods for credit risk analysis ... 10

2.1.1 Judgmental Credit Risk Analysis ... 1 0 2.1.2 Statistical Scoring Model ... 12

2.2 Credit Risk Classification ... 17

2.3 Pitfalls of Automated Classification Modeling ... 18

Chapter Three: ... 19

Study on Feature Selection and Classification '" ... 19

3.1 Introduction to Feature Selection ... 20

3.1.1 Feature selection categories ... 22

3.2 Introduction to Classification ... 30

3.2.1 Supervised machine learning concept.. ... 33

3.2.2 Supervised Classification Methods ... 38

3.2.3 Discussion and comparison between supervised ML methods ... 50

Chapter Four: ... 53

Proposed System Architecture ... 53

4.1 Feature Selection ... 54

4.1.1 Principle Component Analysis- (PCA) ... 55

4.1.2 Support Vector Machine - (SVM) ... 59

4.1.3 The Proposed Genetic Algorithm and SVM Evaluation - (GA -SVM) ... 60

4.2 Decision Making; Classification ... 63

4.2.1 Support Vector Machine Classifier ... 63

4.2.2 SVM Classifier in Credit Risk Application Domain ... 67

4.3 Solutions Description and Graphical Representation '" ... 70

4.3.1 First Solution: (Feature Selection prior to Classification) ... 70

4.3.2 Proposed Solution: GA - SVM method ... 72

Chapter Five: ... 75

Experimental Study and Performance Measurements ... 75 VII

5.1 Data set description ... 76

5.2 Computer Description ... 78

5.3 Results and Discussion ... 79

5.3.1 Analysis of Data ... 80

5.3.2 PCA Feature Selection ... 84

5.3.3 SVM Feature Selection ... 89

5.3.3 The proposed Solution using (GA - SVM) Method ... 95

5.3.4 Comparison of Different Algorithms Results ... 98

5.3.5 Results Summary ... 101

Chapter Six: ... 102

Conclusion and Future Work ... 102

6.1 Conclusion ... 1 03 6.2 Future Work ... 104

References: ... 105

VIII

List of Figures:

Figure 3.1: Wrapper Method ... 26

Figure 3.2: Filter Method ... 28

Figure 3.3: The process of Supervised Machine Learning ... 34

Figure 3.4: Decision Tree ... 38

Figure 3.5: Feed forward ANN ... 44

Figure 3.6: Bayesian Network ... .48

Figure 4.1: SVM Margins between features ... 65

Figure 4.2: Margins ... 67

Figure 4.3: Maximum Margin ... 68

Figure 4.4: Pseudo code for SVMs ... 69

Figure 4.5: Feature Selection prior to classification graphical representation ... ' ... 71

Figure 4.6: GA - SVM Proposed System ... 73

Figure 5.1: Sample features reflecting difficulty in discriminating the two classes ... 81

Figure 5.2: Sample features achieving good separability among classes ... 82

Figure 5.3: Correlation matrix for the 30 features over the whole data set. ... 83

Figure 5.4: The features selected by PCAr method ... 85

Figure 5.5: The Normalized Eigen values for 10 features ... 86

Figure 5.6: The Accumulated Eigen values for the first 10 features and dependability between features ... 87

Figure 5.7: The PCAr criterion for the first 10 features ... 88

Figure 5.8: Sample features selected by the SVM selection plotted for some sample ... 91

IX

Figure 5.9: Worst features as selected by the SVM selection

method ... 93 Figure 5.10: Sample feature discarded by SVM while it achieves acceptable

separability ... '" ... 94 Figure 5.1): Sample feature set obtained by the proposed GA selection

method ... 96 Figure 5.12: Objective function values at different iteration of the GA

module ... 97 Figure 5.13: Error rate for different selection methods using the SVM

classifier. ... 100

x

List of Tables

Table 3.1: Decision Tree Training Data Set. ... .38 Table 5.1: Data Feature Description ... 76 Table 5.2: Error rate obtained from different selected features with different number of features ... 99

XI

List of Symbols and Abbreviations

Abbreviation Description

ANN Artificial Neural Network

ATO Asset Tumover--Represents productivity of the assets.

The higher the ratio is the more efficient the utilization of the assets to generate income.

BN Bayesian Network

B2B Business to Business

Cl Constraint Based

CPT Conditional Probability Table

DAG Directed Acyclic graph

DB Decision Border

DTC Decision Tress Classification

GA Genetic Algorithm

LDA Linear Discriminate analysis

ML Machine Learning

NB NaIve Bayesian

PCA Principle Component Analysis

PCAr Principle Component Analysis Ranking

RMA Risk Management Associate

SVM Support Vector Machine

XII

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108

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ةيفيك قيبطت

هيلآلا

وأ بولسألا حرتقملا

لالخ

قيبطتلا هبرجتل

ةيلمع ضرعو اهجئاتن

بابلا :سداسلا صخلملا

تايصوتلاو ةيلبقتسملا

ه

DIS 76760 C1 006.31

FA-FE

FEATURE SELECTION AND CLASSIFICATION IN CORPORATE BANKING LENDING SYSTEMS

1I1I1III