1. ID3 example

2. No.Risk (Classification)Credit HistoryDebtCollateralIncome 1HighBadHighNone$0 to $15k 2HighUnknownHighNone$15 to $35k 3ModerateUnknownLowNone$15 to $35k 4HighUnknownLowNone$0k to $15k 5LowUnknownLowNoneOver $35k 6LowUnknownLowAdequateOver $35k 7HighBadLowNone$0 to $15k 8ModerateBadLowAdequateOver $35k 9LowGoodLowNoneOver $35k 10LowGoodHighAdequateOver $35k 11HighGoodHighNone$0 to $15k 12ModerateGoodHighNone$15 to $35k 13LowGoodHighNoneOver $35k 14HighBadHighNone$15 to $35k

4. Algorithm for building the decision tree func tree (ex_set, atributes, default) 1. if ex_set = empty then return a leaf labeled with default 2. if all examples in ex_set are in the same class then return a leaf labeled with that class 3. if attributes = empty then return a leaf labeled with the disjunction of classes in ex_set 4. Select an attribute A, create a node for A and labeled the node with A - remove A from attributes –> attributes’ - m = majority (ex_set) -for each value V of A repeat - be partition V the set of examples from ex_set with value V for A - create node V = tree (partition V, atributes’,m) - create link node A - node V and label the link with V end

5. Infordullion theory Universe of messages M = {m 1, m 2,..., m n } and a probability p(m i ) of occurrence of every message in M, the infordullional content of M can be defined as:

6. Infordullional content I(T) p(risk is high) = 6/14 p(risk is moderate) = 3/14 p(risk is low) = 5/14 The infordullional content of the decision tree is: I(Arb) = 6/14log(6/14)+3/14log(3/14)+5/14log(5/14)

7. Infordullional gain G(A) For an attribute A, the infordullional gain obtained by selecting this attribute as the root of the tree equals the total infordullional content of the tree minus the infordullional content that is necessary to finish the classification (building the tree), after selecting A as root G(A) = I(Arb) - E(A)

8. Computing E(A) Set of learning examples C Attribute A with n values in the root -> C devided in {C 1, C 2,..., C n }

9. “Income” as root: C 1 = {1, 4, 7, 11} C 2 = {2, 3, 12, 14} C 3 = {5, 6, 8, 9, 10, 13} G(income) = I(Arb) - E(Income) =1,531 - 0,564 = 0,967 bits G(credit history) = 0,266 bits G(debt) = 0,581 bits G(collateral) = 0,756 bits

10. Learning by clustering Generalization and specialization Learning examples 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -) 10

11. Learning by clustering concept name: NAME positive part cluster: description: (yellow brick nice big) ex: 1 negative part ex: concept name: NAME positive part cluster: description: ( _ _ nice _) ex: 1, 2 negative part ex: 11 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -)

12. Learning by clustering concept name: NAME positive part cluster: description: ( _ _ _ _) ex: 1, 2, 3, 4, 5 negative part ex: 6, 7 12 over generalization 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -)

13. Learning by clustering concept name: NAME positive part cluster: description: (yellow brick nice big) ex: 1 cluster: description: ( blue ball nice small) ex: 2 negative part ex: 6, 7 13 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -)

14. Learning by clustering concept name: NAME positive part cluster: description: ( yellow brick _ _) ex: 1, 3 cluster: description: ( _ ball _ _) ex: 2, 4 negative part ex: 6, 7 14 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -)

15. Learning by clustering concept name: NAME positive part cluster: description: ( yellow _ _ _) ex: 1, 3, 5 cluster: description: ( _ ball _ _) ex: 2, 4 negative part ex: 6, 7 15 1. (yellow brick nice big +) 2. (blue ball nice small +) 3. (yellow brick dull small +) 4. (verde ball dull big +) 5. (yellow cube nice big +) 6. (blue cube nice small -) 7. (blue brick nice big -) A if yellow or ball

16. Learning by clustering 1. Be S the set of examples 2. Create PP and NP 3. Add all ex- from S in NP and remove ex- from S 4. Create a cluster in PP and add first ex+ 5. S = S – ex+ 6. for every ex+ in S e i repeat 6.1 for every cluster C i repeat - Create description e i + C i - if description covers no ex- then add e i to C i 6.2 if e i has not been added to any cluster then create a new cluster with e i end 16