1. Lazy Associative Classification
A. Veloso, W. M. Jr., and M. J. Zaki
ICDM 2006
Advisor: Dr. Koh Jia-Ling
Speaker: Liu Yu-Jiun
Date: 2007/3/8

2. Outline Introduction Information Gain Decision Tree
Eager Associative Classifier
DT v.s. EAC
Lazy Associative Classifier
LAC v.s. EAC
Experiment

3. Introduction Classification problem Models of classification
Decision Tree
Associative Classifier
Neural Network
Genetic Algorithm
Lazy association classifier
DT缺乏宏觀的相關性 (local)
AC有可能產生太多的rule (global)
LAC希望保留AC的準確度且不會產生太多的規則
Lazy的意思是force在有用的features上

4. Information gain S: any subset of training instances.
si: the # of instances with class ci.
|S|: the total # of training instance.
: the probability of class ci in S.
: the entropy of S.
: information gain

5. Decision Tree
A DT is built using a greedy, recursive splitting strategy.
Each internal node is split according to the information gain.
One rule per leaf.

6. Example

7. Decision Tree Classifier
{outlook=sunny and humidity=high  play=no}
{outlook=sunny, temperature=cool, humidity=high, windy=false}

8. Eager Associative Classifier

9. CARs from EAC {windy=false and temperature=cool  play=yes}
{outlook=sunny and humidity=high  play=no}
{outlook=sunny and temperature=cool  play=yes}
{outlook=sunny, temperature=cool, humidity=high, windy=false}

10. DT v.s. EAC

11. Lazy Associative Classifier

12. Projected Training Data

13. Prediction results of EAC and LAC
minsup = 40%
Test instance: {o=overcast, t=hot, h=low, w=true}
{windy=false and humidity=normal  play=yes}
{windy=false and temperature=cool  play=yes}
{temperature=cool and humidity=normal  play=yes}
{outlook=overcast  play=yes}
{temperature=hot  play=yes}
{windy=true  play=no}

14. LAC v.s. EAC

15. Two characteristics
Missing CARs
Highly Disjunctive Spaces

16. Experiment 26 datasets from UCI Machine Learning Repository
min_conf = 50%, min_sup = 1%
Linux-based PC
Intel PIII 1.0 GHz
1G RAM

17. Error Rates
EAC info. gain 絕對比C4.5好，而其他 EAC則不一定，CBA在稀疏資料空間表現比較好，平均而言EAC info gain比CBA好，而CMAR更好的原因在於預測類別時使用多個規則，EAC info gain只有挑rank最高的那個。

18. Rule-Set Utilization

19. Execution Times
Cache size: 10,000 CARs