Memory-Based Reasoning

Memory-Based Reasoning 이재현 PASTA Lab. POSTECH PASTA IE POSTECH

1. Introduction • Memory-Based Reasoning(MBR) is • Identifying similar cases from experience • Applying the information from these cases to the problem at hand. • MBR finds neighbors similar to a new record and uses the neighbors for classification and prediction. • It cares about the existence of two operations • Distance function ; assigns a distance between any two records • Combination function ; combines the results from the neighbors to arrive at an answer. • Applications of MBR span many areas; • Fraud detection • Customer response prediction • Medical treatments • Classifying responses PASTA IE POSTECH

2. How does MBR work? • What is the most likely movie last seen by a respondent based on the source of the record and the age of the individual? • MBR has two distinct phases • The learning phase generates the historical database • The prediction phase applies MBR to new cases PASTA IE POSTECH

2.1. The three main issues in solving a problem with MBR • Choosing the appropriate set of historical records • The historical records, also known as the training set, is a subset of available records. • The training set needs to provide good coverage of the records so that the nearest neighbors to an unknown record are useful for predictive purposes. • Representing the historical records • The performance of MBR in making predictions depends on how the training set is represented in the computer. • Determining the distance function, Combination function, and number of neighbors • The distance function, combination function, and number of neighbors are the key ingredients in determining how good MBR is at producing results. PASTA IE POSTECH

3. Case study ; Classifying News Stories • What are the codes? • News provider assigns codes to news stories in order to describe the content of the stories. These codes help users search for stories of interest. • Applying MBR • Choosing the training set The training set consisted of 49,652 news stories • Choosing the Distance function In this case, a distance function already existed, based on a notion called relevance feedbackthat measures the similarity of two documents based on the words they contain. PASTA IE POSTECH

3. Case study ; Classifying News Stories Relevance Feedback function • Choosing the combination function The combination function used a weighted summation technique. • Choosing the number of neighbors The investigation varied the number of nearest neighbors between 1 and 11 inclusive. PASTA IE POSTECH

3. Case study ; Classifying News Stories • The result • Recall and precision are two measurements that are useful when measuring how well a set of codes get assigned. Recall ; “How many of the correct codes did MBR assign to the story?” Precision ; “How many of the codes assigned by MBR were correct?” PASTA IE POSTECH

4. Measuring Distance • Three most common distance functions • Absolute value of the difference ; |A-B| • Square of the difference ; (A-B)2 • Normalized absolute value |A-B|/(maximum difference) • Example • Gender Dgender(female,female) = 0, Dgender(male,female) = 1 Dgender(female,male) = 1, Dgender(male,male) = 0 PASTA IE POSTECH

4. Measuring Distance • Age • Merge into a single record distance function. Summation ; dsum(A,B) = dgender(A,B) + dage(A,B) + dsalary(A,B) Normalized summation ; dnorm(A,B) = dsum(A,B)/max(dsum) Euclidean distance ; deuclid(A,B) = sqrt(dgender(A,B)2 + dage(A,B)2 + dsalaty(A,B)2) PASTA IE POSTECH

4. Measuring Distance • Set of nearest neighbors for three distance functions • Insert new customer • Gender ; Female, Age ; 45, Salary ; $100,000 • Set of nearest neighbor for new customer PASTA IE POSTECH

5. The combination function ; Asking the neighbors for the answer • The basic approach ; Democracy • The basic combination function used for MBR is to have the K nearest neighbors vote on the answer-”democracy” in data mining. • Customers with Attrition History PASTA IE POSTECH

5. The combination function ; Asking the neighbors for the answer • Using MBR to determining if the new customer will attrite • Attrition prediction with confidence PASTA IE POSTECH

5. The combination function ; Asking the neighbors for the answer • Weighted voting • Weighted voting is similar to voting except that the neighbors are not all created equal • Closer neighbors have stronger votes than neighbors farther away do. • The size of the vote is inversely proportional to the distance from the new record. • To prevent problems when the distance might be 0, it is common to add 1 to the distance before taking the inverse. • Attrition prediction with weighted voting • Confidence with weighted voting PASTA IE POSTECH

6. Conclusion • Strengths of Memory-Based Reasoning • It produces results that are readily understandable. • It is applicable to arbitrary data types, even non-relational data. • It works efficiently on almost any number of fields. • Maintaining the training set requires a minimal amount of effort. • Weaknesses of Memory-Based Reasoning • It is computationally expensive when doing classification and prediction. • It requires a large amount of storage for the training set. • Results can be dependent on the choice of distance function, combination function, and number of neighbors PASTA IE POSTECH

Memory-Based Reasoning