MIRI
Package
weka.classifiers.mi
Synopsis
MIRI (Multi Instance Rule Inducer): multi-instance classifier that utilizes partial MITI trees witha single positive leaf to learn and represent rules. For more information, see
Hendrik Blockeel, David Page, Ashwin Srinivasan: Multi-instance Tree Learning. In: Proceedings of the International Conference on Machine Learning, 57-64, 2005.
Luke Bjerring, Eibe Frank: Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-instance Data. In: Proceedings of the Australasian Joint Conference on Artificial Intelligence, 2011.
From multiInstanceLearning package version 1.0.3 for Weka >= 3.7.2.
Options
The table below describes the options available for MIRI.
Option |
Description |
|---|---|
attributesToSplit |
The number of randomly chosen attributes to consider for splitting. |
b |
Whether to use bag-based statistics for estimates of proportion. |
ba |
Multiplier for count influence of a bag based on the number of its instances. |
debug |
If set to true, classifier may output additional info to the console. |
k |
The value used in the tozero() method. |
l |
Whether to scale based on the number of instances. |
seed |
The random number seed to be used. |
splitMethod |
The method used to determine best split: 1. Gini; 2. MaxBEPP; 3. SSBEPP |
topNAttributesToSplit |
Value of N to use for top-N attributes to choose randomly from. |
unbiasedEstimate |
Whether to used unbiased estimate (EPP instead of BEPP). |
Capabilities
The table below describes the capabilities of MIRI.
Capability |
Supported |
|---|---|
Class |
Missing class values, Binary class |
Attributes |
Relational attributes, Numeric attributes, Empty nominal attributes, String attributes, Date attributes, Nominal attributes, Unary attributes, Binary attributes |
Other |
Only multi-Instance data |
Min # of instances |
1 |