MLEM2

An implementation of MLEM2 rule induction algorithm [1].

How to use

First, we obtain a training data set a.train and a validation data set a.test by 10-fold cross validation.

ruby cv10eqcls.rb -a <attribute file> -i <data file> -d <class name> -p <test position> -s <seed>

<test position> is 0,1,...,9.

We compute a rule set a.rule by the MLEM2 algorithm.

ruby mlem2.rb -a <attribute file> -i <training data file (a.train)> -c <attribute setting file>

Classify the validation data set a.test by the obtained rule set a.rule.

ruby rule_classifier.rb -a <attribute file> -i <validation data file (a.test)> -r <rule file (a.rule)>

For zoo dataset, we set as follows.

<attribute file> := datasets/zoo.attr
<data file> := datasets/zoo.data
<class name> := type
<attribute setting file> := datasets/zoo.cond

Example

Run,

ruby mlem2.rb -a datasets/zoo.attr -c datasets/zoo.cond -i datasets/zoo.data

Then, we obtain the following rules. The last is a default rule.

[type = mammal] <- [milk = true]
	[ mammal(41) bird(0) reptile(0) fish(0) amphibian(0) insect(0) invertebrate(0) ]

[type = bird] <- [feathers = true]
	[ mammal(0) bird(20) reptile(0) fish(0) amphibian(0) insect(0) invertebrate(0) ]

[type = reptile] <- [fins = false] [legs = 0] [tail = true]
	[ mammal(0) bird(0) reptile(3) fish(0) amphibian(0) insect(0) invertebrate(0) ]

[type = reptile] <- [hair = false] [aquatic = false] [legs = 4]
	[ mammal(0) bird(0) reptile(2) fish(0) amphibian(0) insect(0) invertebrate(0) ]

[type = fish] <- [breathes = false] [fins = true]
	[ mammal(0) bird(0) reptile(0) fish(13) amphibian(0) insect(0) invertebrate(0) ]

[type = amphibian] <- [aquatic = true] [toothed = true] [legs = 4] [catsize = false]
	[ mammal(0) bird(0) reptile(0) fish(0) amphibian(4) insect(0) invertebrate(0) ]

[type = insect] <- [aquatic = false] [legs = 6]
	[ mammal(0) bird(0) reptile(0) fish(0) amphibian(0) insect(8) invertebrate(0) ]

[type = invertebrate] <- [airborne = false] [predator = true] [backbone = false]
	[ mammal(0) bird(0) reptile(0) fish(0) amphibian(0) insect(0) invertebrate(8) ]

[type = invertebrate] <- [backbone = false] [legs = 0]
	[ mammal(0) bird(0) reptile(0) fish(0) amphibian(0) insect(0) invertebrate(4) ]

[type = mammal] <-
	[ mammal(41) bird(20) reptile(5) fish(13) amphibian(4) insect(8) invertebrate(10) ]

Reference

[1] J. W. Grzymala-Busse. Rule Induction from Rough Approximations. In: J. Kacprzyk, W. Pedrycz (eds) Springer Handbook of Computational Intelligence. Springer, Berlin, Heidelberg, 2015