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Incremental data mining method based on genetic programming algorithm

A technology of genetic programming and incremental data, applied in the fields of genetic laws, electrical digital data processing, special data processing applications, etc.

Active Publication Date: 2015-02-11
SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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Problems solved by technology

[0004] Although the traditional genetic programming algorithm has no coupling between samples in the learning process, because of the encoding method and evolution mechanism, when the algorithm solves the incremental data, the learning process still needs a lot of cost regardless of the size of the incremental data.

Method used

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  • Incremental data mining method based on genetic programming algorithm
  • Incremental data mining method based on genetic programming algorithm
  • Incremental data mining method based on genetic programming algorithm

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Embodiment Construction

[0050] The present invention will be described in detail below with reference to the accompanying drawings.

[0051] The purpose of the present invention is to better solve the problem of learning ability in incremental data mining. Inheriting the advantages of the genetic programming algorithm, the samples are processed one by one as the input during the learning process, and through the feedback process of multiple intermediate layers, the coefficients and offsets in the model are gradually optimized along the direction of gradient descent, so that the learning ability can be well improved.

[0052] Based on the above considerations, the concrete framework of the present invention is as follows:

[0053] (1) Determine the number of input layers; determine the function composition of the intermediate layer 1; the number of batch samples N sets the number of intermediate layers 2 in the algorithm framework; agree on the evolutionary algebra n in each input network;

[0054] (...

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Abstract

The invention provides an incremental data mining method based on the genetic programming algorithm. Incremental data mining can accomplish incremental model learning task from less to more of data samples; data is input into a model; an input layer performs linear mapping on data, and transmits results into an intermediate layer; the intermediate layer respectively performs nonlinear transformation and space lifting mapping, and outputs results to a voting system; the voting system determines belonging category; a feedback system optimizes network parameters. The learning process is accomplished through multiple iterations. When incremental data is processed, the coupling factor of new and old samples is low, old data samples are not required to be considered when new data is mined, and the method has good succession.

Description

technical field [0001] The invention relates to the field of data mining, in particular to an incremental data mining model. Using the sample batch learning mode, the model combines the advantages of genetic programming and neural networks, which can well realize incremental data mining. Specifically, it is an incremental data mining method based on genetic programming algorithm. Background technique [0002] The large-scale data samples gathered in the era of big data provide possibilities for business model innovation and new scientific discoveries. How to find useful information or resources from large-scale data has become the most important problem to be solved in the era of big data. Data mining is a model discovery Technology is undoubtedly the core technology in the era of big data. [0003] In the process of data sampling, the samples collected within a certain period of time are often limited and have great limitations, and the learning model at this time often a...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06N3/12
CPCG06F16/2465G06N3/126G06N3/044
Inventor 杨振庚吴楠
Owner SHANDONG LANGCHAO YUNTOU INFORMATION TECH CO LTD
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