Fuzzy Query and Ranking with Application to Search Engines

Masoud Nikravesh1
(Professor Lotfi A. Zadeh)
Berkeley Initiative in Soft Computing and and BTExact Technologies (British Telecom)

Ranking/scoring is used to make billions of financing decisions each year serving an industry worth hundreds of billions of dollars. To a lesser extent, hundreds of millions of applications were processed by US universities, resulting in over 15 million college enrollments in 2000, and a total revenue/expenditure of over $250 billion. College enrollments are expected to reach over 17 million by year 2010, a total revenue/expenditure of over $280 billion.

Credit scoring was first developed in the 1950s and has been used extensively in the last two decades. In the early 1980s, the three major credit bureaus, Equitax, Experian, and TransUnion, worked with the Fair Isaac Company to develop generic scoring models that allow each bureau to offer an individual score based on the contents of the credit bureau's data. FICO is used to make billions of financing decisions each year, serving a hundred-billion-dollar industry. Credit scoring is a statistical method to assess an individual's credit worthiness and the likelihood that the individual will repay his/her loans based on his/her credit history and current credit accounts. The credit report is a snapshot of the credit history and the credit score is a snapshot of the risk at a particular point in time. Since 1995, scoring has made its biggest contribution in the world of mortgage lending. Mortgage investors such as Freddie Mac and Fannie Mae, the two main government-chartered companies that purchase billions of dollars of newly originated home loans annually, endorsed Fair Isaac credit bureau risk, ignoring subjective consideration, and agreed that lenders should also focus on other outside factors when making a decision.

When you apply for credit, whether it's a new credit card, car, student loan, mortgage, or financing, about 40 pieces of information from your credit card report are fed into the model. That model provides a numerical score designed to predict your risk as a borrower. When you apply for university/college admission, more than 20 pieces of information from your application are fed into the model. That model provides a numerical score designed to predict your success rate and risk as a student to be admitted.

In this project, we will introduce fuzzy query and ranking to predict the risk in an ever-changing world and imprecise environment, including subjective consideration for several applications including credit scoring, university admission, and locating your favorite restaurant. Fuzzy query and ranking is robust, provides better insight and a bigger picture, contains more intelligence about an underlying pattern in data, and provides the ability of flexible querying and intelligent searching. This greater insight makes it easy for users to evaluate the results related to the stated criterion and makes a decision faster with improved confidence. It is very useful for multiple criteria, and when users want to vary each criterion independently with different confidence degrees or weighting factors. In the case of crisp queries, we can make multi-criterion decisions and ranking where we use the function AND and OR to aggregate the predicates. In the extended Boolean model or fuzzy logic, one can interpret the AND as fuzzy-MIN and OR as fuzzy-MAX functions. Fuzzy querying and ranking is a very flexible tool in which linguistic concepts can be used in the queries and ranking in a very natural form. In addition, the selected objects do not need to match the decision criteria exactly, which gives the system a more human-like behavior. Incorporating an electronic intelligent knowledge-based search engine, the results will be in a format that permits the user to interact dynamically with the contained database and to customize and add information to the database. For instance, it will be possible to test an intuitive concept by dynamic interaction between software and the human mind. This will provide the ability to answer "what if?" questions in order to decrease uncertainty and provide a better risk analysis and, for example, to increase the chance for either admission or increasing score.

M. Nikravesh, "Billions of Transactions for an Industry Worth Hundreds of Billions of Dollars," Intelligent Processing and Manufacturing of Materials,, Vancouver, Canada, July-August 2001.
M. Nikravesh, "Credit Scoring for Billions of Financing Decisions," IFSA/NAFIPS Int. Conf. Fuzziness and Soft Computing in the New Millenium, Vancouver, Canada, July 2001.
M. Nikravesh and B. Azvine, FLINT 2001, New Directions in Enhancing the Power of the Internet, UC Berkeley Electronics Research Laboratory, Memorandum No. UCB/ERL M01/28, August 2001.
L. Vincenzo, M. Nikravesh, and L. A. Zadeh, Journal of Soft Computing, Special Issue: Fuzzy Logic and the Internet, Springer Verlag (to appear).
M. Nikravesh, BISC and The New Millennium, Perception-based Information Processing, Berkeley Initiative in Soft Computing, Report No. 2001-1-SI, September 2001.

More information (http://www-bisc.cs.berkeley.edu) or

Send mail to the author : (nikravesh@cs.berkeley.edu)

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