Predicting and Optimizing the End Price of an Online Auction using Genetic-Fuzzy Approach. This an artificial intelligence methodology to predict and forecast the online auction price to optimal bidding strategy.

1. B Y ,
BUBALAN.V
PRATHEEBAN.R
RAMPRASATH.C
G U I D E D B Y ,
MRS. E.KODHAI, M.E, Ph.D(Pursuing)
ASSOCIATE PROFESSOR
DEPT. OF INFORMATION TECHNOLOGY
Predicting and Optimizing the End Price of an
Online Auction using Genetic-Fuzzy Approach

2. AGENDA
 Introduction
 Literature Survey
 Problem Definition
 Working Model
 System Design
 Module Description
 Experimental Results
 Discussion and Conclusion
 Publication
 References

3. Introduction
Popular mechanism in setting prices for internet users
Popular way of selling used items for scrap value and new items for
profit
Auction price prediction involves the uncertainty regarding the
bidding process
To predict the final prices of English auctions using real-world online
auction data, collected from eBay. Finally, optimize the predicted
values for maximum profit

4. Introduction (cont..)
Why Fuzzy Logic ?
 Logic that deals mathematically with imprecise information
usually employed by humans
 Humans can solve In-deterministic data
 Computers can solve deterministic data
 To make computers to handle the in-deterministic data
There also exists uncertainties in human behaviour while bidding
at auctions

5. Introduction (cont..)
Why Genetic Algorithm ?
Inspired by Darwin theory about evolution “Survival of fittest”
 Search heuristic that mimics the process of natural evolution
Uses Bio Inspired operations( selection, mutation, crossover,
reproduction ) to evolve a solution to a problem
Particularly well suited for Large search space

6. Author’s Title Problem Techniques
Chin-Shien Lin et.al A Final price prediction
model for English
auctions: a neuro-
fuzzy approach
Optimized results will not be
produce if data is inaccurate
• Neural system
• Regression
• fuzzy logic
A.Azadeh et.al A new genetic
algorithm approach for
optimizing bidding
strategy viewpoint of
profit maximization of
generation company
• best only for two players
concern
• should have more knowledge
on rival's strategy
Genetic algorithm
Yun shi liu et.Al Real time prediction of
closing price and
duration Of B2B
reverse auctions
We must employ real time
information and prediction
rules to forecast the behaviour
of live
auctions.
Data mining
Rayid Ghani Price Prediction and
Insurance for Online
Auctions
deals specifically with online
auctions, we believe that this is
an interesting case study that
applies to dynamic markets
where the
price of the goods is variable
and is affected by both internal
and
external factors that change
over time.
Data Mining
Literature Survey

7. Problem Definition
 The drawback of predicting the end price of an online auction in
existing system can be improved by the use of Genetic Fuzzy
approach. The proposed predicting system is a mathematical and
biological concept which takes eBay dataset to predict the end price
using Fuzzy logic and the predicted value is then optimized using
Genetic Algorithm.

8. Working Model
MODIFIED GENETIC ALGORITHM
1.Initiation of Parent population
2.Evaluation for Fittest
3.While Termination Criteria Not Satisfied
{
1.Selection Of child population(Rank Selection)
2.Apply Crossover(Single Point Crossover)
1.Evaluation
2.Replace the result if it is better than previously stored
3.Apply Mutation(flip)
1.Evaluation
2.Replace the result if it is better than previously stored
}
4.Go to Step 3 until termination criteria satisfies

9. Architecture Design

10. User Interface Design

11. Module Description
 Registration Module
contains user information details, authentication of user
 Filtering Module
Filters the auction data collected from e-Bay
Fuzzy Logic Module
contains fuzzy logic rules and processing codes for prediction
 Genetic Module
contains codes for algorithm for population generation , fitness
evaluation ,selection, crossover and mutation

12. Experimental Results

13. Experimental Results(cont..)
Comparison between the results of FL and GA
Price
Bid Price

14. Discussion and Conclusion
Performance Comparison b/w Neuro Fuzzy ,Fuzzy & Genetic Fuzzy Approach
 Performance of the proposed system is calculated using Mean square error
method
 Yi is the actual price and Y^I is the Predicted or Optimized Price and n represents
Observation
 Based on this method the average error of the current system is 0.02181 while the
existing system 0.0735

15. Discussion and Conclusion(cont..)
 Genetic Fuzzy performs the best no matter in the training data sets or
testing data sets.
 To the better prediction accuracy
 Genetic Fuzzy system also provides the knowledge base obtained from
the data set which describes the delicate relationship among the
variables.

16. Publication
 PAPER TITLE
Predicting and Optimizing the End Price of an Online Auction using Genetic - Fuzzy Approach
 JOURNAL NAME
International Journal Of Advanced And Innovative Research
 ISSUE
Vol. 2 Issue 2
 DATE
Febraury, 2013
 PAGE
145 to 150
 ISSUE ISSN
2278-7844

17. References
[1] Chin-Shien Lin, Shihyu Chou, Shih-Min Weng, Yu-Chen Hsieh, “A
Final Price Prediction Model for English Auctions — A Neuro-Fuzzy
Approach”, 2011.
[2] Rechenberg, Ingo. “Evolutionsstrategie”. Stuttgart: Holzmann-
Froboog.,1973, ISBN 3-7728-0373-3.
[3] Srinivas. M and Patnaik. L, "Adaptive probabilities of crossover and
mutation in genetic algorithms," IEEE Transactions on System, Man
and Cybernetics, vol.24, no.4, pp.656–667, 1994.
[4] “A Genetic Algorithm Tutorial”, Darrell Whitley, Computer Science
Department, Colorado State University, Fort Collins, CO 80523.
[5] Rayid Ghani, Hillery Simmons, “Predicting the End-Price of Online
Auctions”, 2004.

18. References (cont..)
[6] Kim, Yongseog, “An Optimal Auction Infrastructure Design: An Agent-
based Simulation Approach,” Proceedings of the Tenth Americas
Conference on Information System, New York, 2004.
[7] Lucking-Reiley, D., “Auctions on the Internet: What’s Being Auctioned,
and How?,” Journal of Industrial Economics, 2000, 48(3).
[8] Pinker, E.J., Seidmann, A. & Vakrat, Y. “Managing Online Auctions:
Current Business and Research Issues,” Management Science, 2003,
49(11), 1457–1484.
[9] Karl Nygren, “Stock Prediction – A Neural Network Approach”, March
2004.
[10] Fei Dong, Sol M. Shatz, Haiping Xu, “An Empirical Evaluation on the
Relationship between Final Auction Price and Shilling Activity in Online
Auctions”, Jan 2011.

19. THANK YOU !