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| Due to the explosive growth of data, the attribute dimensions of the data sets are getting higher, and the volume is larger, which leads to increased training overhead and decreased prediction accuracy of machine learning algorithms. And most of the current attribute reduction algorithms are based on a single attribute reduction, which is not easy to obtain the global optimum and has a large amount of calculation. Based on this, this paper proposes an attribute reduction algorithm based on particle swarm optimization (ARPSO). This algorithm designs the importance function of the attribute set based on the variable precision rough set, and uses particle swarm optimization algorithm to construct the optimization space, optimizes the attribute set in the data set globally, and reduces the redundant attributes of the data set to reduce the training overhead of machine learning algorithms and improve their prediction accuracy. The experimental results show that the attribute reduction performance of the ARPSO algorithm is significantly better than the common attribute reduction algorithms, which verifies the effectiveness of it. |
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| Keywords:Particle swarm; attribute reduction; optimization; ARPSO algorithm |
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