Abstract
In recent years, with the increase in computing power, the sharp drop in costs, and the successful use of data management technology, a large amount of data has been rapidly spread and stored in various fields of the company. How can we passively find active data and form active knowledge from these big data information, know how to use equipment to quickly and accurately obtain high‐quality information, use the obtained information to guide users in decision‐making, and provide more economic and social benefits? This paper focuses on the study of the classifier model based on BP neural network, and the combination of BP neural network model and other optimization algorithms, including genetic algorithm (GA), particle swarm algorithm (PSO), Adaboost algorithm, GA, and PSO have global search performance. It is mostly used to optimize the weight threshold of the network and the number of hidden layer nodes. The Adaboost algorithm builds an enhanced classifier based on the idea of integration. At present, data mining technology has moved from the laboratory research stage to the commercialization stage. The use of widely owned knowledge and information as analysis tools can be used in many fields: such as financial analysis, engineering design, scientific research, management, and production control. At the end of this paper, the improved Adaboost_BP classifier is used, and the result proves that the efficiency of hotel management has increased by at least 75%.
Highlights
With the development of information technology, people’s ability to produce and collect data has been greatly improved
The results show that the learning outcomes of geographic information system (GIS) graduates are more affected by professional courses and computer courses
This article mainly studies the method of constructing data mining classifier based on BP neural network, and the combination with a variety of optimization algorithms, and analyzes through actual cases
Summary
With the development of information technology, people’s ability to produce and collect data has been greatly improved. In the sorting experiment of corn seeds and red wine, the ELM algorithm used in the neural network has the best sorting results, its accuracy is more than 83%, and the modeling time is the shortest He believes that the only choice is to combine specific data volume conditions. Through the data preparation phase, the classifier modeling phase, the mining phase, and the interpretation and analysis phase, the data mining method is applied to the financial early warning system to build a classifier suitable for financial early warning, and through a single BP classifier, Adaboost_BP classifier and the comparison of the classification results of the test samples of the improved Adaboost_BP classifier show that the data using the improved version of the classifier has improved the work efficiency by nearly 75%, which further verifies the effectiveness of the Adaboost algorithm and the improved algorithm
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