Indiana University Bloomington

Luddy School of Informatics, Computing, and Engineering

Technical Report TR717:
Manual for EAR4 and CAAR Weka Plugins, Case-Based Regression and Ensembles of Adaptations, Version 1

Vahid Jalali and David Leake
(Apr 2015), 12 pages pages
EAR4 and CAAR are lazy learners applying the case-based reasoning (CBR) paradigm to numerical prediction tasks. Both augment standard instance-based learning methods by applying automatically generated case adaptation rules to adjust solutions of prior cases, and both apply ensembles of the generated rules. CAAR augments the EAR approach with a richer treatment of case context, more context-aware rule generation, and context-sensitive ranking of the generated adaptation rules. This manual describes installation and use of plugins enabling use of EAR4 and CAAR within the Weka workbench for machine learning.

Available as: