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Data Classification Particle Swarm Optimization and Gravitational Search Algorithm

Shivani Shrivastri , Rahul Deshmukh

Classification is an important problem in data mining. Under the guise of supervised learning, classification has been studied extensively by the AI community as a possible solution to the “knowledge acquisition” or “knowledge extraction” problem. Briefly, the input to a classifier is a training set of records, each of which is a tuple of attribute values tagged with a class label. A set of attribute values defines each record. Many different techniques have been proposed for classification, including Bayesian classification, neural networks, genetic algorithms and tree-structured classifiers. They have been successfully applied to wide range of application areas, such as medical diagnosis, weather prediction, credit approval, customer segmentation, and fraud detection and many more. PSOGSA based data classification can also be apply, might yield more efficient and promising results, work which possesses classification of standard data using gravitational search algorithm with optimize manner. So classification of data done by the famous widely used method Feed-forward neural network with gravitational search algorithm. Particle swarm optimization is a popular heuristic algorithm that had been applied on many optimization problems over the years including data classification problem. The modified PSO is combined with gravitational search algorithm to solve its slow Execution time in the last iterations, making the hybrid PSOGSA algorithm.