Attribute selection method based on binary system firefly algorithm

A firefly algorithm and attribute selection technology, applied in the field of data preprocessing, can solve problems such as low attribute reduction rate, high time complexity of genetic algorithm, and difficulty in determining convergence

Inactive Publication Date: 2016-08-03
HEFEI UNIV OF TECH
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Problems solved by technology

[0005] (1) Using the rough set theory as the evaluation criterion of the attribute subset to be selected, the attribute reduction rate is low, and its time calculation complexity is high;
[0006] (2) In the optimization process, the genetic algorithm has a certain dependence on the selection of the initial population. The selection of the initial population will affect the optimization speed and performance. OK, and the time complexity of the genetic algorithm is high

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  • Attribute selection method based on binary system firefly algorithm
  • Attribute selection method based on binary system firefly algorithm
  • Attribute selection method based on binary system firefly algorithm

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Embodiment Construction

[0064] In this embodiment, an attribute selection method based on the binary firefly algorithm is to apply the discrete binary firefly algorithm to the attribute selection, and proceed as follows:

[0065] Step 1. Use the fractal dimension box counting method to calculate the fractal dimension of the high-dimensional data set with dimension d, and obtain the calculation result d 1 , and rounded up as the number m of selected attributes; m

[0066] A data set usually contains two dimensions such as embedded dimension and fractal dimension. Among them, the embedded dimension is also called Euclidean dimension, which refers to the dimension of the Euclidean space embedded in the data point, that is, the number of attribute indicators contained in the data set; Levy dimension is also called fractal dimension, which reflects the number of key attribute indicators needed to express a data set. In practical applications, fractal dimension is generally used to approximate the intr...

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Abstract

The invention discloses an attribute selection method based on a binary system firefly algorithm. The attribute selection method is characterized in comprising the following steps: 1: utilizing a fractal dimension box-counting method to calculate a fractal dimension of a high-dimension data set, and obtaining the number of attributes which need to be selected; 2: initializing a firefly population; 3: utilizing the binary system firefly algorithm to select a plurality of attributes of the high-dimension data set to obtain an optimal attribute subset; 4: outputting an optimal solution. The attribute selection method uses the binary system firefly algorithm as a search strategy of attribute selection, takes the fractal dimension as an attribute selection evaluation measurement criterion, and selects the optimal attribute subset from the plurality of index attributes of the high-dimension data set, so that the data processing complexity can be lowered, and the data processing efficiency is improved so as to meet the requirements of solving practical problems.

Description

technical field [0001] The invention relates to the field of data preprocessing in data mining and pattern recognition, specifically an attribute selection method based on a binary firefly algorithm. Background technique [0002] Data mining (Data Mining) refers to the behavior of exploring effective information hidden in data through a series of complex algorithms from massive data, and the high-dimensionality and multi-attributes of data will affect the performance of data mining. Therefore, it is necessary to perform dimensionality reduction preprocessing on high-dimensional data sets, so as to improve the efficiency of data mining. The so-called data dimensionality reduction is to select a most representative attribute subset from the attribute set of the original data set according to certain criteria, also known as attribute selection. Attribute selection is based on the correlation and redundant relationship measurement between attribute variables and target attribut...

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Application Information

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IPC IPC(8): G06F17/30G06N3/00
CPCG06F16/2465G06N3/006
Inventor 倪志伟李敬明张琛朱旭辉金飞飞伍章俊
Owner HEFEI UNIV OF TECH
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