Enhanced relationship classifier based on representative samples

A relational classification and representative technology, applied in the field of pattern recognition, can solve the problems that the relational matrix R cannot correctly reflect the characteristics of data distribution, the classification lacks robustness, and cannot correctly and truly reflect the logical relationship between categories and structures.

Inactive Publication Date: 2012-11-28
NANJING NORMAL UNIVERSITY
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Problems solved by technology

When the data set contains many class overlapping regions, R constructed in this way cannot correctly and truly reflect the logical relationship between categories and structures, resulting in the following defects in FRC: lack of robustness of classification; degradati

Method used

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  • Enhanced relationship classifier based on representative samples
  • Enhanced relationship classifier based on representative samples
  • Enhanced relationship classifier based on representative samples

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Embodiment Construction

[0030] The present invention has first set following experimental operating conditions:

[0031] 1. Each dimension feature in the data set is normalized to the interval [0, 1] through the maximum and minimum normalization method;

[0032] 2. The number of clusters c in EFRC is in [c min , c max ] determined within the range, where c min is the number of categories, c max for (N is the number of samples).

[0033] 3. X new The ratio λ to the original set X is chosen in the range (0,1).

[0034] Based on the above conditions, the enhanced relational classifier based on representative samples proposed by the present invention has been implemented in the scientific computing platform Matlab, and the effectiveness of the method is proved by the experimental results in Matlab.

[0035] The concrete method flowchart of EFRC classifier of the present invention is as figure 1 shown. Below in conjunction with accompanying drawing, further describe the specific implementation s...

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Abstract

The invention relates to an enhanced relationship classifier based on representative samples. The method mainly comprises two steps: first, selecting representative samples to form a new training sample set Xnew according to the clustering membership of samples; and then, constructing a fuzzy relationship matrix R with a Phi composite operator about the clustering membership and the class membership of Xnew. The enhanced relationship classifier is mainly characterized in that (1) the matrix R can reveal the inherent logic relationship between a cluster and a class; (2) the computation complexity of the matrix R decreases from O(NLc) to O(MLc), wherein L is the number of classes, c is the number of clusters, N is the number of samples of the original dataset X, M is the number of samples of Xnew, and N is greater than M; and (3) when sufficient judgment information cannot be found in certain areas in the sample space, the classifier rejects to make strategies for test samples falling into the areas, so as to guarantee the confidence level of classification results.

Description

technical field [0001] The invention belongs to the field of pattern recognition, in particular to a relation classifier based on cluster analysis. Background technique [0002] The main task of pattern recognition is to process and analyze various forms of information representing transactions or phenomena, so as to classify (or group) and explain things or phenomena. The field of traditional pattern recognition contains two important research topics, namely unsupervised clustering and supervised classification. [0003] Supervised classification aims to design a class discriminant function based on the given data and its class label, so that it can make correct predictions for the class of unknown samples. This type of method focuses on the category assignment of samples, which can lead to relatively good generalization for unseen samples. However, this type of algorithm only emphasizes the classification of sample individuals, while ignoring the mining of hidden structu...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 蔡维玲
Owner NANJING NORMAL UNIVERSITY
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