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Data classification method based on self-set density search and clustering detector-negative selection algorithm (DSC-NSA)

A technology of data classification and negative selection, applied in computing, computer parts, instruments, etc., can solve the problems of small time overhead, low false positive rate, high false positive rate, and achieve the goal of small time cost, elimination of influence, and elimination of noise. Effect

Inactive Publication Date: 2017-02-22
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the disadvantages of the existing data classification method based on the negative selection algorithm that the influence of noise cannot be eliminated, the time overhead is large, and the false positive rate is high, the present invention provides a method that effectively eliminates the influence of noise, has small time overhead, and has a high false positive rate. Data classification method based on self-set density search and partition clustering negative selection algorithm with low judgment rate

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  • Data classification method based on self-set density search and clustering detector-negative selection algorithm (DSC-NSA)
  • Data classification method based on self-set density search and clustering detector-negative selection algorithm (DSC-NSA)
  • Data classification method based on self-set density search and clustering detector-negative selection algorithm (DSC-NSA)

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] refer to Figure 1 ~ Figure 4 , a data classification method based on the negative selection algorithm of self-set density search and partition clustering, the data classification method includes the following steps:

[0049] 1) Calculate the cluster center and delete the noise according to the density peak value of the data set;

[0050] 2) Generate a self-detector from non-noisy self-samples;

[0051] 3) Generate non-self detectors from self detectors;

[0052] 4) Use the self-detector and the non-self-detector at the same time to judge whether the detected data samples are abnormal, and realize data classification.

[0053] refer to figure 1 and figure 2 , the self-set clustering process is as follows:

[0054] 1.1) Calculate the ρ of each point in the data set according to the formula i and δ i , ρ i is the density of the i-th data point, δ i is t...

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Abstract

The invention discloses a data classification method based on a self-set density search and clustering detector-negative selection algorithm (DSC-NSA). The method comprises the following steps: 1), according to a density peak of a data set, calculating a cluster center and deleting noise; 2), according to a non-noise self-sample, generating a self-detector; 3), according to the self-detector, generating a non-self detector; and 4), determining whether a detection data sample is abnormal by simultaneous use of the self-detector and the non-self detector so as to realize data classification. The data classification method based on the self-set DSC-NSA, provided by the invention effectively eliminate influences of noise and is quite small in time cost and quite low in misjudgement rate.

Description

technical field [0001] The invention relates to a data classification method. Background technique [0002] The artificial immune system is a simulation of the biological immune system, with learning ability, memory ability and powerful information processing ability. Inspired by the biological immune system, AIS draws on the functions and principles of the immune system and applies it to solving complex problems. It is the earliest artificial immune system model. Negative selection algorithm (NSA: Negative selection algorithm) is an important detector generation algorithm in artificial immune theory. It is derived from the model of T cells maturing in the thymus and has the ability to identify self and abnormality. Negative Selection Algorithm (NNSA) was first proposed by FORREST S in 1994. NNSA is based on string representation, but its application is limited due to the impact of computational overhead. RNSA normalizes the properties of detectors and antigens to the N-di...

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Application Information

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IPC IPC(8): G06K9/62G06K9/40
CPCG06V10/30G06F18/23G06F18/24
Inventor 陈晋音苏蒙蒙章涛陈军敢杨东勇俞山青
Owner ZHEJIANG UNIV OF TECH
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