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Optimized Convolutional Gabor Using Particle Swarm Optimization : Case Study: Vehicle Classification Tasks

The increase of data is becoming faster, so the amount of data becomes very large, which in the end creates a big data problem. Three aspects that are often identified as problems faced with big data are volume, velocity, and variety. In this paper, the big data problem that will be solved is the problem of volume, which is quantity/ amount of data. Several common methods for solving the problem are compressing the data, reducing the dimensionality feature, and managing the workflow storage. This paper applies the Particle Swarm optimization, an evolutionary algorithm, in dimensionality feature reduction. The case study used in this paper is image data with classification task. We exhibit the framework by applying particle swarm optimization algorithm in the reduction feature. In practice, such frameworks can not only be done for data images but can be used for other data with classification task. This paper compares the performance with and without feature selection. From the results of the comparison, feature selection achieves a better accuracy and a faster computation time.