A NOVEL DISCRIMINATION PREVENTION SYSTEM USING FUZZY LOGIC

Under the guidance ofMr. R. Balamurugan,Assistant Professor of CSE,APEC.

Presented byP. Ashwini,ME CSE (II year)APEC.

ABSTRACT

An education loan is designed to help students pay for university tuition fee, books,

and hostel. Generally education loan are granted based on only marks and this leads

to discrimination among students. The main objective of my work is to give

automated decision making system that eliminates discrimination using fuzzy logic

for education loan system. A fuzzy approach is exploited in our intelligent system,

that handles uncertainty and provides a precise decision just like human way of

reasoning.

INTRODUCTION (1/2)

• Data mining and knowledge discovery in databases are two new research areas

that deal with the automatic extraction of useful patterns from large amounts of

data.

• Data mining techniques are used in business and research and are becoming more

and more popular with time.

• There are two issues related to data mining.

• These issues are privacy violation and potential discrimination.

INTRODUCTION (2/2)

• Discrimination is a very important issue when considering the legal and ethical

aspects of data mining.

• It can be viewed as the act of illegally treating people on the basis of their

belonging to a specific group.

• Data mining must not become a source of discrimination, since automated

decision systems learn from data mining models.

EXISTING SYSTEM (1/2)

• In recent literatures, Sara Hajian et al(2013), T.Calders et al(2010) have proposed

the antidiscrimination techniques such as discrimination discovery and

discrimination prevention.

• Discrimination discovery is the extraction of discriminatory situations and

practices hidden in a large amount of historical decision records.

• Discrimination prevention is a transformation technique that identifies

discriminatory biases contained in the original data and removes it.

EXISTING SYSTEM-PITFALLS (2/2)

• Antidiscrimination technique in the existing system do not include any

measure to evaluate how much discrimination has been removed and how

much information loss has been incurred.

• This approach cannot guarantee that that the transformed data is discrimination

free.

PROPOSED SYSTEM (1/2)

• The main focus of my work is to give automated decision making system for

education loan processing using the fuzzy inference system.

• In Fuzzy inference system, the crisp input are converted into linguistic variable

using the membership function stored in the fuzzy knowledge base. This is

known as fuzzification.

• Using the if-then type of rules convert fuzzy input to fuzzy output.

• The fuzzy output converted back to a crisp output using the membership

functions, in the defuzzification step.

PROPOSED SYSTEM – ADVANTAGES (2/2)

• Discrimination is prevented without any data loss.

• Clear decision can be made as the selection process is not based on the single

factor but the combination of them.

• Automation leads to the reduction of time consumption.

DATABASE

CRISP OUTPUT

FUZZIFICATION

KNOWLEDGE BASED SYSTEM

DEFUZZIFICATION

FUZZY INFERENCE SYSTEMSYSTEM ARCHITECTURE

INFERENCEKNOWLEDGE RULE BASE

CRISP INPUT

SOFTWARE REQUIREMENTS

Operating System : Windows 7 Technology : Java and J2EE Web Technologies : Html 5, JavaScript, CSS IDE : My Eclipse Web Server : Tomcat Database : My SQL

HARDWARE REQUIREMENTS

Processor : AMD Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB Floppy Drive : 1.44 MB Key Board : Standard Windows Keyboard

DATAFLOW DIAGRAMLEVEL 0

LEVEL 1

LEVEL 2

MODULES

• USER MODULE

• FUZZY INFERENCE SYSTEM

• MANAGER MODULE

USER MODULE

• Input: Mark, income, graduate.

• Output: Loan status

• In this module user enter the complete information such as personnel details,

mark, income, graduation, qualification details etc., to apply loan.

• Later the user can login to check the eligibility to apply for education loan.

NEW USER REGISTRATION

FUZZY INFERENCE SYSTEM

INPUT : Let C ={C1,C2,C3…Cn} be set of all linguistic variables(Candidate Details) OUTPUT : Crisp Output

STEPS:

Step 1: Initialize the linguistic variables and terms.Step 2: Determine the degree of truth from the crisp inputs.Step 3:Take the fuzzified inputs and apply them to the antecedents of the fuzzy rules.Step 4: If the fuzzy rule has multiple antecedents, the fuzzy operator AND or OR is used to obtain the truth value.Step 5: Apply the truth value to the consequent triangular member function.Step 6: Combine the membership functions of all rule consequents into a single fuzzy setStep 7: Defuzzify the fuzzy set into a crisp output.

FUZZY RULES

FUZZY CHART

MANAGER MODULE

• Input : Login details of the manager.

• Output: Loan status of all students obtained from FIS

• In this module manager can view list of students who have applied for loan.

• He also checks the complete details of a specific student and their loan status

gained from fuzzy inference system.

MANAGER PAGE

REFERENCE1. Sara Hajian and Josep Domingo-Ferrer, Fellow, “A Methodology for Direct and Indirect

Discrimination Prevention in Data Mining”, IEEE Transactions On Knowledge And Data Engineering, Vol. 25, No. 7, July 2013.

2. S. Hajian, J. Domingo-Ferrer, and A. Martinez-Balleste, “Rule protection for indirect discrimination prevention in data mining,” in Modeling Decision for Artificial Intelligence, pp. 211-222, Springer, 2011

3. F. Kamiran and T. Calders, “Classification with no discrimination by preferential sampling,” in Proc. 19th Machine Learning Conf. Belgium and The Netherlands, 2010.

4. Ying Bai and Dali Wang, “Fundamentals of Fuzzy Logic Control – Fuzzy Sets,Fuzzy Rules and Defuzzifications”