Other
Classification Methods
Genetic Algorithms
o
Genetic Algorithm: based on an analogy to
biological evolution
o
An initial population
is created consisting of randomly generated rules
·
Each rule is represented by a string of bits
·
E.g., if A1 and ¬A2 then C2 can be encoded as 100 o If an attribute has k > 2
values, k bits can be used
o
Based on the notion of survival of the fittest, a new population is formed to
consist of the fittest rules and their offsprings
o
The fitness of a rule is represented by its classification accuracy on a set of
training examples
o
Offsprings are generated by crossover and mutation
o
The process continues until a population P evolves when each rule in P satisfies a prespecified threshold
o
Slow but easily parallelizable
Rough Set Approach:
o
Rough sets are used to approximately or ―roughly‖ define equivalent classes
o
A rough set for a given class C is approximated by
two sets: a lower approximation (certain to be in C) and an upper approximation
(cannot be described as not belonging to C)
o
Finding the minimal subsets (reducts) of attributes for feature reduction is NP-hard but a discernibility matrix (which stores the
differences between attribute values for each pair of data tuples) is used to reduce the computation intensity
Figure: A rough set approximation
of the set of tuples of the class C suing lower and upper approximation sets of
C. The rectangular regions represent equivalence classes
Fuzzy Set approaches
o
Fuzzy logic uses truth values between 0.0 and 1.0
to represent the degree of membership (such as using fuzzy membership graph)
o
Attribute values are converted to fuzzy values
e.g., income is mapped into the discrete categories
{low, medium, high} with fuzzy values calculated
o
For a given new sample, more than one fuzzy value
may apply
o
Each applicable rule contributes a vote for
membership in the categories
o
Typically, the truth values for each predicted
category are summed, and these sums are combined
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