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A Feature Selection Method for Pattern Classification

A feature selection method and pattern classification technology, applied in the field of pattern recognition

Active Publication Date: 2016-09-21
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0008] The purpose of the present invention is to address the problems existing in the existing selection methods, and to provide a feature selection method for pattern classification based on the unsupervised optimal discriminator vector to achieve data dimensionality reduction in an unsupervised mode

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  • A Feature Selection Method for Pattern Classification
  • A Feature Selection Method for Pattern Classification
  • A Feature Selection Method for Pattern Classification

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Embodiment Construction

[0070] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0071] Such as image 3 Shown: the present invention scheme realizes the feature selection of pattern classification by following each steps:

[0072] A. Convert the original data into an N×d matrix, where N is the number of samples and d is the feature dimension;

[0073] B. Given threshold ε or number of iterations α, given feature importance threshold θ, where the value range of threshold ε is [0.001, 0.01], the value range of iteration number α is [20, 50], feature importance The value interval of the threshold θ is [0.8, 0.95];

[0074] C. Use the k-means algorithm to initialize the membership matrix U=[μ ij ] c×N And the cluster center m=(m 1 ,m 2 ,...,m c ), where u ij Indicates the degree to which the j-th sample belongs to the i-th class, c is the number of split clusters, where i and j are variables and the value intervals are: [1, c],...

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Abstract

The invention discloses a feature selecting method for pattern classification. The method includes on the basis of figuring out a unsupervised optimal discriminant vector by adopting fuzzy Fisher criterion as an objective function, figuring out each feature importance weight according to a value of each dimension of the vector, collating the features according to dimension of the weights, selecting subsets of the features through a given threshold, and accordingly realizing feature dimension reduction. According to the feature selecting method, sample classify information is not required to provide beforehand, the problem that feature selection is lack of separation measurement in a unsupervised mode is solved effectively, good dimension reduction performances are realized in UCI data set and fault diagnosis experiments, and the feature selecting method is highly practical.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a feature selection method for pattern classification, which can be applied to feature dimensionality reduction in industries such as data mining and fault diagnosis. Background technique [0002] Feature selection removes redundant features, constructs feature subsets, and achieves data dimensionality reduction, which can not only reduce computing costs, but also improve classification accuracy. Research hotspots. [0003] Feature selection can be divided into supervised feature selection and unsupervised feature selection according to whether the sample category information is known. For the supervised feature selection method, since the sample category information is known, an evaluation function can be defined to measure the classification accuracy of each subset, so that the feature subset only includes those features that can guide the correct classification of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 曹苏群朱全银左晓明高尚兵陈晓峰张虹杨格兰陈召兴
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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