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1. Machine learning was used to create a decision tree model to diagnose problems in telecommunications networks, achieving 99% accuracy with only 10,000 examples. 2. The model was simplified for comprehensibility, becoming probabilistic and covering 50% of cases with general rules and 50% with specific small disjuncts. 3. Lessons from the success include the importance of model comprehensibility, handling small datasets, addressing systematic errors, and considering future extensions when applying machine learning solutions.

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Machine Learning meets the Real World: Successes and new research directions Andrea Pohoreckyj Danyluk Department of Computer Science Williams College, Williamstown, MA October 11, 2002
Data, data everywhere... ,[object Object],[object Object],[object Object],[object Object]
A wealth of information ,[object Object],[object Object],[object Object],[object Object],[object Object]
A wealth of information ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Machine learning success (Machine learning is ubiquitous) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why research in machine learning is so good today ,[object Object],[object Object],[object Object],[object Object],[object Object]
Plan for this talk ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Induction of decision trees ,[object Object],[object Object],[object Object]
Inductive learning ,[object Object],[object Object]
Sample data
Predictive model I.e., g<x>
Learning objectives ,[object Object],[object Object],[object Object]
TDIDT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Which is better?
The Gain Criterion ,[object Object],[object Object],[object Object]
Information (Entropy) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Information (Entropy) ,[object Object],[object Object],[object Object]
Entropy after a split ,[object Object],[object Object]
Information Gain ,[object Object],[object Object],[object Object]
Which is better?
Scrubber (the success story) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MAX, 1990 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scrubber 2 ,[object Object],[object Object],[object Object],[object Object]
Scrubber 3 ,[object Object],[object Object],[object Object]
Implementation difficulties ,[object Object],[object Object],[object Object],[object Object],[object Object]
Requirements ,[object Object],[object Object],[object Object],[object Object],[object Object]
Additional requirements (ours) ,[object Object],[object Object]
Phase I: Modeling Scrubber 2 ,[object Object],[object Object],[object Object],[object Object]
Data ,[object Object],[object Object],[object Object],[object Object],[object Object]
Background knowledge ,[object Object],[object Object]
Phase I results ,[object Object],[object Object]
Phase II: Acceptance ,[object Object],[object Object],[object Object],[object Object],[object Object]
Trading off simplicity and correctness ,[object Object],[object Object],[object Object],[object Object]
Phase II results ,[object Object],[object Object],[object Object]
Phase III: Working toward extensibility ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Phase IIIb: More data ,[object Object],[object Object],[object Object]
Phase III results ,[object Object],[object Object],[object Object],[object Object]
Summarizing the success story ,[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons can be learned from success ,[object Object],[object Object],[object Object],[object Object]
Lessons can be learned from success ,[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons can be learned from success ,[object Object],[object Object],[object Object],[object Object]
Lessons can be learned from success ,[object Object],[object Object],[object Object],[object Object]
Lessons can be learned from success ,[object Object],[object Object],[object Object]
Further reading and acknowledgements ,[object Object],[object Object],[object Object]

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Editor's Notes

  1. Introduction: My research in the area of Artificial Intelligence. People sometimes imagine AI as robots -- or more generally computer programs that are smarter than we are. AI is really a huge area that includes things such as robotics, natural language, learning, etc., but it’s also applicable to areas that are very different from what you might immediately imagine.
  2. Give some sense of what “gigabytes” means.
  3. Now that we’ve discussed data mining a bit, let’s switch gears and talk about machine learning. We can then begin to see how the two fit together in a very natural way. One goal of AI research has been to build general-purpose intelligent systems. Expert systems are not general purpose -- rather, they are specialized for a particular expert task.
  4. Now that we’ve discussed data mining a bit, let’s switch gears and talk about machine learning. We can then begin to see how the two fit together in a very natural way. One goal of AI research has been to build general-purpose intelligent systems. Expert systems are not general purpose -- rather, they are specialized for a particular expert task.
  5. Give lots of explanation of the examples. Begin with the cancer recurrence example, even though it isn’t listed here.
  6. Give lots of explanation of the examples. Begin with the cancer recurrence example, even though it isn’t listed here.
  7. Consider reading this as a set of rules. The “model” captures a set of patterns found in the data.
  8. The important question, of course, is how is a program supposed to figure out the patterns? Let’s start by asking ourselves this question: which of these is better?
  9. Write on the slides some numbers -- lowest is best.
  10. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  11. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  12. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  13. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  14. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  15. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  16. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  17. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  18. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  19. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  20. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  21. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  22. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  23. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.
  24. The diagnostic model can answer questions like: what pattern of symptoms looks like a cable failure? Which patterns looks like a problem with the wiring inside your house? Etc? Can be used to diagnose problems that weren’t seen before. Can tell them about Max, Scrubber, etc.