Method for predicting air quality grade by integrating sequence pattern mining and cost sensitive learning

A technology that is sensitive to air quality levels and costs. It is applied in the field of level prediction and can solve problems such as uniform treatment of air quality levels, so as to improve prediction performance and reduce negative impacts.

Inactive Publication Date: 2018-03-02
HANGZHOU SUNKING TECH CO LTD
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Problems solved by technology

However, such methods also have the following problems: First, most existing methods train discriminative models (such as decision trees, support vector machines, etc.)
In fact, the classification error of

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  • Method for predicting air quality grade by integrating sequence pattern mining and cost sensitive learning
  • Method for predicting air quality grade by integrating sequence pattern mining and cost sensitive learning
  • Method for predicting air quality grade by integrating sequence pattern mining and cost sensitive learning

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[0044] Example: such as figure 1 As shown, an air quality grade prediction method that combines sequential pattern mining and cost-sensitive learning includes three steps:

[0045] (1) Sequential pattern mining, constructing sequence pattern tree;

[0046] Such as figure 2 As shown, in the step (1), given the air quality grade historical sequence data AS, the detailed steps of sequence pattern mining are as follows:

[0047] (1-1) Initialize the projection database: first find out all frequent air quality levels from AS (that is, air quality levels whose occurrences are greater than the specified threshold δ); then, based on AS, perform a calculation of each frequent air quality level a 1 Generate projection data; finally all generated projection data constitute the initial projection database PS.

[0048] Among them, based on AS to a 1 The method to generate projection data is: first search for a 1 At all occurrence positions in AS; then for each occurrence position i, intercept the...

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Abstract

The invention relates to a method for predicting the air quality grade by integrating sequence pattern mining and cost sensitive learning, which comprises the steps of firstly mining a sequence pattern from historical sequence data of the air quality grade, and building a sequence pattern tree; then extracting features from air quality and meteorological historical data, and training a cost sensitive prediction model based on a cost sensitive learning technology; and finally integrating the sequence pattern mode and the cost sensitive prediction model for final air quality grade prediction. The method takes a changing pattern of the air quality grade and the unbalanced prediction error cost into consideration on the basis of the existing air quality grade prediction method based on machinelearning, and can effectively improve the prediction performance of the model.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an air quality level prediction method that combines sequential pattern mining and cost-sensitive learning. Background technique [0002] Air pollution is a very serious urban problem at present. Monitoring and forecasting air quality level is of great significance for pollution assessment, pollution control and harm reduction. At present, the air quality level is monitored in real time through air quality monitoring stations, and the prediction of air quality level needs to be realized by designing a reasonable calculation model. [0003] The existing air quality level prediction methods mainly fall into the following two categories: [0004] (1) Atmospheric pollutant diffusion calculation model: It is an empirical method, which calculates the air quality level at different locations after a period of time through parameters such as pollutant concentration, wind direct...

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Application Information

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IPC IPC(8): G06Q10/04G06N99/00
CPCG06N20/00G06Q10/04
Inventor 吕明琪陈岭李一帆张圣陈铁明
Owner HANGZHOU SUNKING TECH CO LTD
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