Multi-label classification algorithm research based on swarm intelligence

Abstract

Since data and resources have massive feature and feature of data are increasingly complex, traditional data structures are not suitable for current data anymore. Therefore, traditional single-label learning method cannot meet the requirements of technology development and the importance of multi-label leaning method becomes more and more highlighted. K-Nearest Neighbor (KNN) classification method is a lazy learning method in data classification methods. It does not need data training process and theoretical system is mature. In addition, principle and implementation is simple. This paper proposed improvements strategies only considers numerical feature of sample KNN when classifying, but not consider the disadvantage of sample structure feature. This paper introduced particle swarm optimization algorithm into KNN classification and make adjustments to Euclidean distance formula in traditional KNN classification algorithm and add weight value to each feature. Using adjusted distance formula to train training data through particle swarm optimization algorithm and optimized a set of weight value for all features and put these optimized weight values to adjusted distance formula and calculated the distance between each example in test data set and in training data set and predict the test data set. Experiment results show that weighted KNN classification algorithm based on particle swarm optimization algorithm can achieve better classification accuracy than traditional KNN classification algorithm.