Logistics enterprise customer classification method based on semi-supervised kernel Fisher discriminant analysis

Abstract

The invention discloses a logistics enterprise customer classification method based on semi-supervised kernel Fisher discriminant analysis. The logistics enterprise customer classification method is characterized by comprising the following steps: (1) determining customer classification indexes and classification conditions commonly used by logistics enterprises; (2) collecting logistics enterprise customer information according to the customer classification indexes determined in the step (1); (3) standardizing the data sample set in the step (2); (4) constructing a consistency hypothesis matrix for the normalized customer sample data matrix obtained in the step (3), and calculating local inter-class and intra-class Laplace matrixes; (5) calculating a regularization term Laplacian matrixby utilizing the consistency hypothesis matrix obtained in the step (4), integrating the regularization term Laplacian matrix into a Fisher discriminant analysis target function, and obtaining an optimal projection matrix by solving a minimized target function; (6) calculating the projection coordinates of the normalized customer sample in the step (2) on the projection matrix; and (7) classifyingthe projection coordinates by using a nearest neighbor algorithm to determine the customer category. The logistics enterprise customer classification method is applied to classification of logisticsenterprise customers.

Description

Technical field: [0001] The application of the present invention relates to the field of logistics enterprise customer management, in particular to a logistics enterprise customer classification method based on semi-supervised kernel Fisher discriminant analysis. Background technique: [0002] In recent years, with the rapid development of IT and communication technologies such as the Internet, the Internet of Things, cloud computing, and triple play, big data has begun to spread to all walks of life in society. The advent of the era of big data has brought about major changes in the operating environment of domestic logistics companies. At present, domestic logistics is facing a market environment that is fully open and fully competitive at home and abroad. In the competitive business era, logistics companies must possess more advantageous resources, have a large number of effective customers, provide the best customer service, improve the original customer experience, and ...

AI Technical Summary

Problems solved by technology

However, FDA and KFDA are supervised dimensionality reduction algorithms, which require a large amount of label information in advance to obtain better generalization performance.

However, in real life, especially labeling massive high-dimensional customer data of logistics companies is a very time-consuming and laborious work, so there are often situations where only a small number of labeled samples exist and a large number of unlabeled samples remain.

Due to the lack of enough labeled samples, FDA and its improved algorithms usually suffer from overfitting, which leads to a serious decline in generalization performance.

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