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Feature selection approach based on neural-fuzzy network

A feature selection method and neuro-fuzzy technology, applied in the field of pattern recognition, can solve problems such as data distortion

Inactive Publication Date: 2016-03-09
SHANGHAI DIANJI UNIV
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Problems solved by technology

However, since the definition of its membership function is actually done before network learning, the membership mapping may still cause the mapped data to be distorted

Method used

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  • Feature selection approach based on neural-fuzzy network
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  • Feature selection approach based on neural-fuzzy network

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Embodiment Construction

[0024] In order to make the content of the present invention clearer and easier to understand, the content of the present invention will be described in detail below in conjunction with specific embodiments and accompanying drawings.

[0025] The present invention proposes a new type of neuro-fuzzy network, which is based on the combination of fuzzy set theory and artificial neural network, and is mainly used for new feature selection technology. This method can be applied to the fields of pattern recognition, data mining, image processing, etc. .

[0026] The principles and embodiments of the present invention will be explained below starting from the neuro-fuzzy network structure.

[0027] (1) Neuro-fuzzy network structure:

[0028] For class C (ω 1 ,ω 2 ,…ω l ,…,ω C ) recognition problem, record the training sample set as, χ = { x q | x q = ...

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Abstract

The invention provides a feature selection approach based on a neural-fuzzy network. The feature selection approach includes the following steps of: the first step, adjusting parameters of fuzzy membership functions to obtain a membership function set shown in the description, based on an x training neural fuzzy network, wherein [mu]fimj represents an mj-th membership function of features fi; the second step, calculating the output oq of the neural-fuzzy network when the input is xq; the third step, modifying the neural-fuzzy network to allow a fuzzy mapping layer to map the xq as that shown in the description, wherein the values of all the membership functions of the fi are identically equal to 0.5, and at this moment, the input of the neural-fuzzy network is that shown in the description; the fourth step, calculating measurement values FQJi shown in the description of the feature fi; and the fifth step, sorting the feature measurement values FQJi in a descending order.

Description

technical field [0001] The present invention relates to the technical field of pattern recognition, more specifically, the present invention relates to a feature selection method based on neural fuzzy network. Background technique [0002] Feature selection is a key problem in techniques such as pattern recognition. In recent years, the use of artificial neural networks for feature selection is a hot issue. Through artificial neural networks with good learning performance, the importance of individual features or feature subsets can be inferred, but there are some problems. [0003] In the field of feature selection, most feature selection based on artificial neural networks can be regarded as a special case of network pruning algorithm, the difference is that the input nodes are pruned instead of hidden layer nodes or weights. At present, the widely used method is to use the change amount between the network output before and after pruning as the measure of the importance...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06N3/08
CPCG06N3/08G06V30/194
Inventor 胡静
Owner SHANGHAI DIANJI UNIV
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