Computational Intelligence, SS08
2 VO 442.070 + 1 RU 708.070 last updated:
General
Course Notes (Skriptum)
Online Tutorials
Practical Course Slides
Homework
Assignments
Scores
Guidelines
Archive
Exams
Animated Algorithms
Interactive Tests
Key Definitions
Downloads
Literature and Links
News
mailto:webmaster

Homework 39: Comparison of different learning algorithms



[Points: 12.5; Issued: 2006/05/11; Deadline: 2006/05/31; Tutor: Arian Mavriqi; Infohour: 2006/05/29, 13:00-14:00, HSi13; Einsichtnahme: 2006/06/12, 13:00-14:00, HSi13; Download: pdf; ps.gz]





This homework assignment asks you to compare the performance of three learning algorithms on different data sets by using the WEKA toolkit1. Choose three data sets from the archiv contained in the file datasets.zip. Compare the following three algorithms





Category WEKA implementation
Majority/Average predictor ZeroR
Decision trees j48.J48
Instance based learning IBk
Support vector machines SMO




  • Choose an appropriate method to compare the algorithms.
  • Explain what you did so that the results can be reproduced by everyone.
  • Present your results clearly, structured and legibly.
  • State for each data set which learning algorithm you would recommend and explain why. Note: Consider not only the error on the test set, but also criteria as for instance the time for learning, interpretability of the hypothesis etc.




Fußnoten

... toolkit1
See the links section on the CI homepage for further information and tutorials about WEKA.