Classification in Complex Systems

1
Classification in Complex Systems

• Why we should look at the paper
• CAEP Classification by Aggregating Emerging
  Patterns
• G. Dong, X. Zhang, L. Wong, and J Li

2
What are Common Problems in Classification?

• Many variables
• Graphs that relate tuples
• Protein-protein interactions (KDD-cup 02)
• Citations (KDD-cup 03)
• Anything that violates standard table format

3
Many Variables

• Solution
• Naïve Bayes way of multiplying probabilities
• Other additive models
• Problems
• Many factors
• May be correlated
• Noise
• but it gets worse

4
Graphs

• 2 kinds of attributes
• Attributes within nodes
• Attributes of neighbor and more distant nodes
• How do neighbor attributes count?
• Take disjunction?
• At least one neighbor that has a particular
  property
• Probably preferable
• Use links or, more general, paths as basis
• Integration into classification???

5
Idea

• Get away from strict set of n attributes
• If an attribute or combination of attributes is
  interesting use them
• Combining rules?
• I would have guessed as in Naïve Bayes
• CAEP adds probabilities!?

6
What is interesting

• CAEP paper claims growth rate
• Support of a rule increases significantly from
  one class label to another
• Note Only increase, not decrease!
• What does that mean?
• For pattern e and classes P and N
• growth_rateP?N (e)
• suppN (e) / suppP (e)

7
2 Things Worth Investigating

• Is interestingness measure related to
  information gain?
• Under certain assumptions Yes
• Can the score be justified?
• Sum of P(C)!?

8
Other Issues

• Normalization
• Emerging patterns only consider increase in
  support gt different number of relevant patterns
• How to mine for EPs

9
Conclusions

• Idea very valuable
• Classification split into ARM-step and rule
  combination
• Justification of details?
• Not great
• Should be possible to do it right with poorer
  accuracy -)