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Tournament

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Tournament

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  1. Tournament • Not complete • Processing will begin again tonight, 7:30PM until wee hours • Friday, 8-5. • Extra Credit • 5 points for passing screening, in tournament • 5 points for top 60% (top 16) • 10 points for top 8 • 10 points for top 4 • 10 points for top 1 • Celebration next Tuesday: it’s a party! • Be prepared to talk about the evaluation function you used • What worked • Next class: tutorial on machine learning tools

  2. Medical Application of Bayesian Networks:Pathfinder

  3. Pathfinder • Domain: hematopathology diagnosis • Microscopic interpretation of lymph-node biopsies • Given: 100s of histologic features appearing in lymph node sections • Goal: identify disease type malignant or benign • Difficult for physicians

  4. Pathfinder System • Bayesian Net implementation • Reasons about 60 malignant and benign diseases of the lymph node • Considers evidence about status of up to 100 morphological features presenting in lymph node tissue • Contains 105,000 subjectively-derived probabilities

  5. Commercialization • Intellipath • Integrates with videodisc libraries of histopathology slides • Pathologists working with the system make significantly more correct diagnoses than those working without • Several hundred commercial systems in place worldwide

  6. Sequential Diagnosis

  7. Features • Structured into a set of 2-10 mutually exclusive values • Pseudofollicularity • Absent, slight, moderate, prominent • Represent evidence provided by a feature as F1,F2, … Fn

  8. Value of information • User enters findings from microscopic analysis of tissue • Probabilistic reasoner assigns level of belief to different diagnoses • Value of information determines which tests to perform next • Full disease utility model making use of life and death decision making • Cost of tests • Cost of misdiagnoses

  9. Group Discrimination Strategy • Select questions based on their ability to discriminate between disease classes • For given differential diagnoisis, select most specific level of hierarchy and selects questions to discriminate among groups • Less efficient • Larger number of questions asked

  10. Other Bayesian Net Applications • Lumiere – Who knows what it is?

  11. Other Bayesian Net Applications • Lumiere • Single most widely distributed application of BN • Microsoft Office Assistant • Infer a user’s goals and needs using evidence about user background, actions and queries • VISTA • Help NASA engineers in round-the-clock monitoring of each of the Space Shuttle’s orbiters subsystem • Time critical, high impact • Interpret telemetry and provide advice about likely failures • Direct engineers to the best information • In use for several years • Microsoft Pregnancy and Child Care • What questions to ask next to diagnose illness of a child

  12. Machine Learning Reading: Chapter 18

  13. Machine Learning and AI • Improve task performance through observation, teaching • Acquire knowledge automatically for use in a task • Learning as a key component in intelligence

  14. Inductive Learning • Input: x, f(x) • Output: a function h that approximates f • A good hypothesis, h, is a generalization or learned rule

  15. How do systems learn? • Supervised • Unsupervised • Reinforcement

  16. Three Types of Learning • Rule induction • E.g., decision trees • Knowledge based • E.g., using a domain theory • Statistical • E.g., Naïve bayes, Nearest neighbor, support vector machines

  17. Applications • Language/speech • Machine translation • Summarization • Grammars • IR • Text categorization, relevance feedback • Medical • Assessment of illness severity • Vision • Face recognition, digit recognition, outdoor scene recognition • Security • Intrusion detection, network traffic, credit fraud • Social networks • Email traffic • To think about: applications to systems, computer engineering, software?

  18. Language Tasks • Text summarization • Task: given a document which sentences could serve as the summary • Training data: summary + document pairs • Output: rules which extract sentences given an unseen document • Grammar induction • Task: produce a tree representing syntactic structure given a sentence • Training data: set of sentences annotated with parse tree • Output: rules which generate a parse tree given an unseen sentence

  19. IR Task • Text categorization • http://www.yahoo.com • Task: given a web page, is it news or not? • Binary classification (yes, no) • Classify as one of business&economy,news&media, computer • Training data: documents labeled with category • Output: a yes/no response for a new document; a category for a new document

  20. Medical • Task: Does a patient have heart disease (on a scale from 1 to 4) • Training data: • Age, sex,cholesterol, chest pain location, chest pain type, resting blood pressure, smoker?, fasting blood sugar, etc. • Characterization of heart disease (0,1-4) • Output: • Given a new patient, classification by disease

  21. General Approach • Formulate task • Prior model (parameters, structure) • Obtain data • What representation should be used? (attribute/value pairs) • Annotate data • Learn/refine model with data(training) • Use model for classification or prediction on unseen data (testing) • Measure accuracy

  22. Issues • Representation • How to map from a representation in the domain to a representation used for learning? • Training data • How can training data be acquired? • Amount of training data • How well does the algorithm do as we vary the amount of data? • Which attributes influence learning most? • Does the learning algorithm provide insight into the generalizations made?

  23. Classification Learning • Input: a set of attributes and values • Output: discrete valued function • Learning a continuous valued function is called regression • Binary or boolean classification: category is either true or false

  24. Learning Decision Trees • Each node tests the value of an input attribute • Branches from the node correspond to possible values of the attribute • Leaf nodes supply the values to be returned if that leaf is reached

  25. Example • http://www.ics.uci.edu/~mlearn/MLSummary.html • Iris Plant Database • Which of 3 classes is a given Iris plant? • Iris Setosa • Iris Versicolour • Iris Virginica • Attributes • Sepal length in cm • Sepal width in cm • Petal length in cm • Petal width in cm

  26. Summary Statistics: Min Max Mean SD ClassCorrelation sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) • Rules to learn • If sepal length > 6 and sepal width > 3.8 and petal length < 2.5 and petal width < 1.5 then class = Iris Setosa • If sepal length > 5 and sepal width > 3 and petal length >5.5 and petal width >2 then class = Iris Versicolour • If sepal length <5 and sepal width > 3 and petal length  2.5 and ≤ 5.5 and petal width  1.5 and ≤ 2 then class = Iris Virginica

  27. Data

  28. Data

  29. Constructing the Decision Tree • Goal: Find the smallest decision tree consistent with the examples • Find the attribute that best splits examples • Form tree with root = best attribute • For each value vi (or range) of best attribute • Selects those examples with best=vi • Construct subtreei by recursively calling decision tree with subset of examples, all attributes except best • Add a branch to tree with label=vi and subtree=subtreei

  30. Construct example decision tree

  31. Issues • Representation • How to map from a representation in the domain to a representation used for learning? • Training data • How can training data be acquired? • Amount of training data • How well does the algorithm do as we vary the amount of data? • Which attributes influence learning most? • Does the learning algorithm provide insight into the generalizations made?