WEKA

1. WEKA

2. Weka: the bird Copyright: Martin Kramer (mkramer@wxs.nl) University of Waikato

3. University of Waikato

4. University of Waikato

5. University of Waikato

6. Hamilton University of Waikato

7. WEKA: the software • Waikato Environment for Knowledge Analysis • Collection of state-of-the-art machine learning algorithms and data processing tools implemented in Java • Released under the GPL • Support for the whole process of experimental data mining • Preparation of input data • Statistical evaluation of learning schemes • Visualization of input data and the result of learning • Used for education, research and applications • Complements “Data Mining” by Witten & Frank University of Waikato

8. Main Features • 49 data preprocessing tools • 76 classification/regression algorithms • 8 clustering algorithms • 15 attribute/subset evaluators + 10 search algorithms for feature selection • 3 algorithms for finding association rules • 3 graphical user interfaces • “The Explorer” (exploratory data analysis) • “The Experimenter” (experimental environment) • “The KnowledgeFlow” (new process model inspired interface) University of Waikato

9. History • Project funded by the NZ government since 1993 • Develop state-of-the art workbench of data mining tools • Explore fielded applications • Develop new fundamental methods University of Waikato

10. History - timeline • Late 1992 - funding was applied for by Ian Witten • 1993 - development of the interface and infrastructure • WEKA acronym coined by Geoff Holmes • WEKA’s file format “ARFF” was created by Andrew Donkin • ARFF was rumored to stand for Andrew’s Ridiculous File Format • Sometime in 1994 - first internal release of WEKA • TCL/TK user interface + learning algorithms written mostly in C • Very much beta software • Changes for the b1 release included (among others): “Ambiguous and Unsupported menu commands removed.” “Crashing processes handled (in most cases :-)” • October 1996 - first public release of WEKA (v 2.1) University of Waikato

11. History - timeline • July 1997 - WEKA 2.2 • Schemes: 1R, T2, K*, M5, M5Class, IB1-4, FOIL, PEBLS, support for C5 • Included a facility (based on unix makefiles) for configuring and running large scale experiments • Early 1997 - decision was made to rewrite WEKA in Java • Originated from code written by Eibe Frank for his PhD • Originally codenamed JAWS (JAva Weka System) • May 1998 - WEKA 2.3 • Last release of the TCL/TK-based system • Mid 1999 - WEKA 3 (100% Java) released • Version to complement the Data Mining book • Development version (including GUI) University of Waikato

12. Back then… University of Waikato

14. Today:

15. Explorer: pre-processing the data • Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary • Data can also be read from a URL or from an SQL databases using JDBC • Pre-processing tools in WEKA are called “filters” • WEKA contains filters for: • Discretization, normalization, resampling, attribute selection, attribute combination, … University of Waikato

16. Explorer: Building classification models • “Classifiers” in WEKA are models for predicting nominal or numeric quantities • Implemented schemes include: • Decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, Bayes’ nets, … • “Meta”-classifiers include: • Bagging, boosting, stacking, error-correcting output codes, data cleansing, … University of Waikato

17. Explorer: classification University of Waikato

18. Explorer: classification University of Waikato

19. Explorer: classification University of Waikato

20. Explorer: classification University of Waikato

21. Explorer: classification University of Waikato

22. Explorer: classification University of Waikato

23. Explorer: classification University of Waikato

24. Explorer: classification University of Waikato

25. Explorer: classification University of Waikato

26. KnowledgeFlow: process flows University of Waikato

27. KnowledgeFlow: batch processing University of Waikato

28. KnowledgeFlow: batch processing University of Waikato

29. KnowledgeFlow: incremental processing University of Waikato

30. Experimenter University of Waikato

31. Experimenter University of Waikato

32. Experimenter University of Waikato

33. Impact - downloads University of Waikato

34. Projects based on WEKA • Incorporate/wrap WEKA • GRB Tool Shed - a tool to aid gamma ray burst research • YALE - facility for large scale ML experiments • GATE - NLP workbench with a WEKA interface • Judge - document clustering and classification • Extend/modify WEKA • BioWeka - extension library for knowledge discovery in biology • WekaMetal - meta learning extension to WEKA • Weka-Parallel - parallel processing for WEKA • Grid Weka - grid computing using WEKA • Weka-CG - computational genetics tool library University of Waikato

35. The WEKA Project Today • FRST funding for the next two years • Goal of the project remains the same • People • 6 staff • 2 postdocs • 3 PhD students • 3 MSc students • 2 research programmers University of Waikato

36. The Future • Continue to develop and support WEKA • MOA (Massive Online Analysis) • Framework that supports learning from data streams • Facilities for data generation, experimental analysis, learning algorithms, etc. • The Moa (another native NZ bird) is not only flightless, like the Weka, but also extinct • First public release, probably this Christmas, or perhaps Thanksgiving (as it’s just another turkey) • MILK • Multi-Instance Learning Kit • Proper • Propositionalization toolbox for WEKA University of Waikato