Multi-label attribute classification algorithm for big databased on core density estimation

Aiming at the problems of inaccurate core density estimation of tag attributes during the classification process, resulting in large attribute feature extraction errors, low classification accuracy, and poor data balance coefficient, a large data multi tag attribute classification algorithm based on core density estimation is designed. First, the machine learning algorithm is used to extract data attribute features, calculate their expected estimates, and complete feature extraction. Then, the extracted data is processed in a unified dimension, and the mean value processing and Pearson correlation coefficient are used to complete the pre-processing. Finally, according to the core density estimation theory, determine the density of any data in the overall density function, then determine the probability quality function and degradation increment of other characteristic data points, introduce the classification kernel function, and construct the classification model to achieve the final effective classification. The results show that the proposed method can reduce feature extraction error and improve classification accuracy.