Discovery of Classification Rules using Genetic Algorithm with non-random Population initialization



Goal of classification technique is to predict the class which an instance of dataset belongs to. Discovered knowledge is then presented in the form of high level, easy to understand classification rules. Genetic algorithm has been widely adopted and applied for discovery of classification rules.  The main criticism of employing genetic algorithms in data mining applications is local convergence and algorithm may become a random walk in initial runs. One solution to this problem is giving a filtering bias to initial population such that more significant attributes get initialized with higher probability as compared to less significant attributes. This paper proposes a genetic algorithm with non-random population initialization. Each attribute in the initial population is initialized with a probability proportional to its entropy such that more the entropy less significant the attribute is and, then a survival probability factor is also considered to make the population better. Relevant attributes occurring more frequently in the initial population provides a good start for GA to search for better fit rules at earlier generations and thus time utilization is noted.


Rule mining, non-random initialization, entropy based, classification rule

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