CSC 4510/9010: Applied Machine Learning. Rule Inference. Dr. Paula Matuszek

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1 CSC 4510/9010: Applied Machine Learning 1 Rule Inference Dr. Paula Matuszek Paula.Matuszek@villanova.edu Paula.Matuszek@gmail.com (610)

2 Classification rules Popular alternative to decision trees Antecedent (pre-condition): a series of tests (just like the tests at the nodes of a decision tree) Tests are usually logically ANDed together (but may also be general logical expressions) Consequent (conclusion): classes, set of classes, or probability distribution assigned by rule Individual rules are often logically ORed together Conflicts arise if different conclusions apply Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 3) 2

3 Rules in AI 3 Mycin: Shortliffe and Buchanan, mid-1980s Hand-crafted rules for diagnosing blood infections Triggered the idea of If..Then rules which modeled human expertise Emycin: empty Mycin : The inference component of Mycin, without the actual rules. Expert Systems (ES) Knowledge base (KB) Inference engine Effective, still in extensive use

4 Example Rules in an ES 4 Rule Class1: If: It is snowing, and SEPTA is not running Then: It is possible that 4510 will not meet (0.7) Rule Class2: If There is an exam scheduled Then It is possible that 4510 will meet (0.8) Rule Class3: IF Villanova cancels class THEN It is possible that 4510 will not meet (1.0)

5 Some Problems Here 5 Time consuming to elicit, typically requires both domain expert and knowledge engineer Not trivial to handle conflict resolution Looks like the inference is straight logic and probabilities, but in fact people are terrible at both All of these mean that it would be nice to find some way to create the rules automatically: rule induction

6 Inductive Logic 6 Deductive logic: If it s Tuesday, then Paula is teaching It is Tuesday. Therefore Paula is teaching Sound if premises are true, conclusion is always true. Inductive logic: Paula taught 8/30, 9/6, 9/13, 9/20, 9/27. That is every Tuesday this semester. Therefore Paula is teaching on Tuesdays. If it s Tuesday then Paula is teaching Paula is teaching. Therefore it is Tuesday. Not sound. But often useful.

7 Simple Rule Induction 7 Given Features Training examples Output for training examples Generate automatically a set of rules which will allow you to judge new objects Basic approach is Combinations of features become antecedents or links Examples become consequents or nodes

8 Simple Rule Induction Example 8 Starting with 100 cases, 10 outcomes, 15 variables Form 100 rules, each with 15 antecedents and one consequent. Collapse rules. Cancellations: If we have C, A => B and C, A => B, collapse to A => B Drop Terms: D, E => F and D, G => F, collapse to D => F Test rules and undo collapse if performance gets worse Additional heuristics for combining rules.

9 Rose Diagnosis 9 Yellow Leaves Wilted Leaves Brown Spots Fungus N Y Y Bugs N Y Y Nutrition Y N N Fungus N N Y Fungus Y N Y Bugs Y Y N R1: If not yellow leaves and wilted leaves and brown spots then fungus. R6: If wilted leaves and yellow leaves and not brown spots then bugs

10 Rose Diagnosis 10 Cases 1 and 4 have opposite values for wilted leaves, so create new rule: R7: If not yellow leaves and brown spots then fungus. KB is rules. Learner is system collapsing and test rules. Critic is the test cases. Performer is rule-based inference. Problems: Over-generalization Irrelevance Need data on all features for all training cases Computationally painful. Useful if you have enough good training cases. Output can be understood and modified by humans

11 Alternate Approach: Covers 11 Rather than start with one rule/example, we can start with a rule that includes, or covers, all of one class and excludes the others. Pick ad attribute and a value that comes closest; expand rule or add rules to capture additional cases. Repeat for additional classes PRISM, RIPPER, JRip in Weka; pp in text. Similar to a decision tree algorithm, but bottom-up instead of top-down.

12 Induced Rule Sets 12 Positives: A covering set of rules can match a consistent dataset perfectly. Human-Readable, and even modifiable. White-box Negatives Tend to overfit Computationally difficult For a large dataset or many attributes, can end up with a complex set of rules. And conflict resolution is still an issue Can we make it simpler?

13 Simplicity first Simple algorithms often work very well! There are many kinds of simple structure, eg: One attribute does all the work All attributes contribute equally & independently A weighted linear combination might do Instance-based: use a few prototypes Use simple logical rules Success of method depends on the domain Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 13

14 Inferring rudimentary rules 1R: learns a 1-level decision I.e., rules that all test one particular attribute Basic version One branch for each value Each branch assigns most frequent class Error rate: proportion of instances that don t belong to the majority class of their corresponding branch Choose attribute with lowest error rate (assumes nominal attributes) Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 14

15 Pseudo-code for 1R For each attribute, For each value of the attribute, make a rule as follows: count how often each class appears find the most frequent class make the rule assign that class to this attribute-value Calculate the error rate of the rules Choose the rules with the smallest error rate Note: missing is treated as a separate attribute value Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 15

16 Evaluating the weather attributes Outlook Temp Humidity Windy Play Sunny Sunny Overcast Rainy Rainy Rainy Overcast Sunny Sunny Rainy Sunny Overcast Overcast Hot Hot Hot Mild Cool Cool Cool Mild Cool Mild Mild Mild Hot High High High High Normal Normal Normal High Normal Normal Normal High Normal True True True True True No No No No Attribute Outlook Temp Humidity Windy Rules Sunny No Overcast Rainy Hot No* Mild Cool High No Normal True No* Errors Rainy Mild High True No * indicates a tie Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 16 2/5 0/4 2/5 2/4 2/6 1/4 3/7 1/7 2/8 3/6 Total errors 4/14 5/14 4/14 5/14

17 Dealing with numeric attributes Discretize numeric attributes Divide each attribute s range into intervals Sort instances according to attribute s values Place breakpoints where class changes (majority class) This minimizes the total error Example: temperature from weather data No No No No No Outlook Sunny Sunny Overcast Rainy Temperature Humidity Windy Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 17 True Play No No

18 The problem of overfitting Discretization is very sensitive to noise One instance with an incorrect class label will probably produce a separate interval Also: time stamp attribute will have zero errors Simple solution: enforce minimum number of instances in majority class per interval Example (with min = 3): No No No No No No No No No No Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 18

19 Discussion of 1R 1R was described in a paper by Holte (1993) Contains an experimental evaluation on 16 datasets (using cross-validation so that results were representative of performance on future data) Minimum number of instances was set to 6 after some experimentation 1R s simple rules performed not much worse than much more complex decision trees Simplicity first pays off! Very Simple Classification Rules Perform Well on Most Commonly Used Datasets Robert C. Holte, Computer Science Department, University of Ottawa Slides from the Weka text can be found at Data Mining: Practical Machine Learning Tools and Techniques (Chapter 4) 19

20 Rules and Trees 20 A decision tree can be turned into rules: walk the tree and AND the tests. One rule for each leaf. Unambiguous rules, not order dependent However, unnecessarily complex Rules to decision trees is harder. Trees do not capture OR well; If x = 3 then class = A If X = 4 then class = A If rules have conflicts, how does it get encoded? Top-Down (DT) and Bottom-Up (Rules) can lead to different models

21 Rules Summary 21 Multiple approaches, but the basic idea is the same: infer simple rules that make the decision based on logical combinations of attributes ZeroR: predict the most common class OneR is a good first test. For simple domains the rules are easy to understand by humans Sensitive to noise, overfitting Not a good fit for complex domains, large number of attributes

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