Genetic Algorithm: based on an analogy to biological evolution

__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 A_{1} and ¬A_{2} then C_{2} can be encoded as 100 o If an attribute has k > 2
values, k bits can be used

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^{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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Data Warehousing and Data Mining : Association Rule Mining and Classification : Other Classification Methods |

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