Research on data mining algorithm of logistics time series based on intelligent integrated network structure

Zhengyi Zhang,Jian Cui

doi:10.1186/s13638-020-01715-3

Abstract

With the continuous development of information, big data analysis has become important and dependent technical means-increasingly in various fields. By data mining through time series, the development regular of the object could be grasped, so we could predict its future development trend. Based on the intelligent integration architecture, a new algorithm of bi-weighted support vector machines (SVM) based on category weighting, and feature weighting was proposed to solve the problem of unbalanced samples in time series. In the non-balanced sample set classification, the recognition ability of the traditional classification method was low; the supported vector machine as classifier was taken in the new algorithm based on cost-sensitive learning, and different weighting coefficients to less and more samples were given, and Gauss kernel function with the weight coefficients of different features was reconstructed, thus the recognition ability of less samples was improved. In the experiment, classification accuracy, g-mean, f-measure, TP, and FP were selected as evaluation indexes, indicating that the two-weighted SVM algorithm is effective in the classification of non-balanced sample sets.

Highlights

With the continuous development of information, big data analysis has become important and dependent technical means-increasingly in various fields
Traditional methods include linear regression analysis, nonlinear regression analysis, auto-regressive sliding average (ARMA) modeling, partial least square method, and gray prediction. The intelligent technology such as expert system, fuzzy rules, neural network, and support vector machine is used in the intelligent method to realize the predictive modeling [3, 4]
5 Results and discussion In this paper, a dual-weighted support vector machine model DWSVM based on sample class weighting and sample feature weighting is proposed, and the verification experiment is carried out on the actual data set

Summary

Introduction

With the continuous development of information, big data analysis has become important and dependent technical means-increasingly in various fields. Traditional methods include linear regression analysis, nonlinear regression analysis, auto-regressive sliding average (ARMA) modeling, partial least square method, and gray prediction. The intelligent technology such as expert system, fuzzy rules, neural network, and support vector machine is used in the intelligent method to realize the predictive modeling [3, 4]. For the time-series data of a kind of random noise disturbance, an auto-regression sliding average model of nested dual-population particle swarm algorithm is proposed by using a parallel nested modeling structure [8]. A single modeling method can mine part of the information in the time series data to know the corresponding law. The smart integrated architecture diagram is as follows (Fig. 1) [17]:

Methods

Kernel function based on feature weighting

Results and discussion