A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
Classification rule mining from huge amount of data is a challenging issue in data mining. Classification rules describe the relationship between predicting attributes and class label attribute and thus assign class label to unseen predicting attribute values. In this paper, a genetic algorithm approach with modified fitness function for discovering classification rules has been presented. A flexible encoding scheme for representing a rule, genetic operators like crossover, mutation and also the stated fitness function with confidence, coverage, simplicity and interestingness properties have been exploited for discovering accurate, comprehensible and interesting rules.
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