A fuzzy K-nearest neighbor classification method and system based on weighted chi-square distance metric

A chi-square distance, classification method technology, applied in character and pattern recognition, instruments, computer parts and other directions, can solve the problem of classification accuracy affected by noise, unweighted data, low classification accuracy, etc., to ensure the accuracy rate , The effect of reducing classification time and accurate classification

Inactive Publication Date: 2019-01-22
QILU UNIV OF TECH
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Problems solved by technology

[0006] The technical task of the present invention is to address the above deficiencies, providing a fuzzy K-nearest neighbor classification method and system based on the weighted chi-square distance measure, to solve the problem that the existing fuzzy K-nearest neighbor algorithm has a low classification accuracy rate, and the classification accuracy rate is affected by noise and irrelevance. Data and issues with unweighted data impact

Method used

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  • A fuzzy K-nearest neighbor classification method and system based on weighted chi-square distance metric
  • A fuzzy K-nearest neighbor classification method and system based on weighted chi-square distance metric
  • A fuzzy K-nearest neighbor classification method and system based on weighted chi-square distance metric

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Embodiment 1

[0061] attached figure 1 As shown, a fuzzy K-nearest neighbor classification method based on the weighted chi-square distance metric of the present invention calculates the weight of each feature according to the fuzzy closeness of the close distance type, takes the weighted chi-square distance as the distance measure, and passes k neighbors The class membership degree determines the category of the sample to be classified, and evaluates the classification result to obtain the sample that meets the evaluation effect. The specific steps of the algorithm are as follows.

[0062] Step S100, establishing test samples and training samples, specifically as follows:

[0063] The training sample set is X={(x i ,c i )|i=1,2,...,n}, wherein, i=1,2,...n, i is the variable of the number of training samples, n is the number of training samples; x i Indicates the i-th training sample, x i l is an l-dimensional vector, that is, the feature dimension is l, x i l Indicates the l-th f...

Embodiment 2

[0131] A fuzzy K-nearest neighbor classification system based on weighted chi-square distance measurement, including sample construction module, neighbor number setting module, weighted chi-square distance calculation module, sample category determination module and sample evaluation module; sample construction module is used to construct samples, including Training samples and test samples; the neighbor number setting module is used to set the value of the neighbor number; the weighted chi-square distance calculation module can read the constructed sample from the sample construction module, and can read the set neighbor number from the neighbor number setting module value, and can calculate the weighted chi-square distance between the training sample and the test sample; the sample category determination module can read the constructed sample from the sample construction module, and can read the value of the neighbor number from the neighbor number setting module, and can read...

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Abstract

The invention discloses a fuzzy K-nearest neighbor classification method and system based on weighted chi-square distance metric, belonging to the field of fuzzy K-nearest neighbor algorithm. The technical problem to be solved is that the classification accuracy of the existing fuzzy K-nearest neighbor algorithm is low, and the classification accuracy is influenced by noise, irrelevant data and unweighted data. The method comprises the following steps: establishing a training sample and a test sample, and setting a value of a nearest neighbor number k; Calculating weighted chi-square distance;with weighted chi-square distance between the training sample and the test sample as the distance measure, by using the class membership degree of k nearest neighbor training samples,determining theclass of the test sample; evaluating the class. The system includes a sample building module, a nearest neighbor number setting module, a weighted chi-square distance calculation module, a sample category determination module and a sample evaluation module. The classification time is short, the classification accuracy is not affected by noise, relative data and unweighted data, and the stability of the method is high.

Description

technical field [0001] The present invention relates to the field of fuzzy K-nearest neighbor algorithms, in particular to a fuzzy K-nearest neighbor classification method and system based on a weighted chi-square distance measure. Background technique [0002] In the classification method, KNN is an inert classification learning algorithm, that is, the learning model is not constructed until a test sample is given. It classifies according to the class of samples that are the majority among the K nearest neighbor samples to the test sample. The specific process is as follows: Assume that each category contains multiple samples, and each sample belongs to a specific category. KNN first calculates the distance between each known sample and the sample to be divided, and then takes the category that is the majority of the K samples closest to the sample to be divided as the category of the sample to be divided. Ideally, a distance metric must minimize the distance between two ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24147G06F18/214
Inventor 王新刚姚培培
Owner QILU UNIV OF TECH
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