1 / 19

Lazy Associative Classification

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

vaughn
Download Presentation

Lazy Associative Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

  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

  18. Rule-Set Utilization

  19. Cache size: 10,000 CARs Execution Times

More Related