Algorithm for Key Classification Feature Selection of Big Data Based on Henie Theorem

With the extensive application of the database system, the available data of enterprises or individuals are expanding, and the existing technology is difficult to meet the data analysis requirements of the big data age. Therefore, the selection of key classification features of big data needs to be carried out. However, when the key classification features of big data are selected by the current algorithm, the distance between the samples can not be given accurately, and there is a large error in the classification. To solve this problem, a key classification feature selection algorithm based on Henie theorem is proposed. In this algorithm, the second programming algorithm is firstly used to make the weighted distance between the intra-class and the inter-class as the quadratic term and linear term parameter in the target function, and balance the relationship between the data features and the different categories. The optimized vector is used as the weight vector to measure the contribution of the feature to the classification. According to the feature importance degree, the redundancy feature is gradually deleted, and the problem of selecting the key classification features of big data into the resolution principle is fused into the Henie theorem. The function limit and sequence limit of the key classification features of big data are obtained. Based on this, the key classification features of big data are selected. Experimental simulation shows that the proposed algorithm has higher classification accuracy and can effectively meet the needs of data analysis in the era of big data.