Local learning feature weight selection-based medical data classification method and device

A technology for medical data and feature selection, applied in the field of medical diagnosis, can solve the problems that the convergence cannot be guaranteed and the algorithm computational complexity is high, and achieve the effect of reducing computational complexity, ensuring convergence, and reducing complexity.

Inactive Publication Date: 2017-09-22
SUZHOU UNIV
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Problems solved by technology

However, in the application of this algorithm to noisy data and high-level data, the conv

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  • Local learning feature weight selection-based medical data classification method and device
  • Local learning feature weight selection-based medical data classification method and device
  • Local learning feature weight selection-based medical data classification method and device

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Embodiment Construction

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0049] see figure 1 , the embodiment of the present invention discloses a medical data classification method based on local learning feature weight selection. specifically:

[0050]S101: Obtain a first sample set of medical data, and obtain attributes of the first sample.

[0051] Specifically, to obtain the first sample set of medical data The sample attribute of the first sample set is obtained as the first sample attribute. where x i ∈R I ,y i ∈{1,2,...,...

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Abstract

The invention discloses a local learning feature weight selection-based medical data classification method. The method comprises the following steps of: firstly obtaining attributes of samples according to a training sample set, and calculating weight vectors corresponding to attributes according to the attribute values by utilizing a gradient descent weight updating manner so as to ensure the astringency, achieve a stopping criterion of an algorithm in a relatively high speed, shorten the calculation time and reduce the calculation complexity; and carrying out feature selection according to the calculated weight vectors so as to obtain an optimum feature set, standardizing to-be-assessed data samples and carrying out feature selection in an optimum feature subset, and classifying the to-be-assessed data sample after the feature selection so as to realize dimensionality reduction of the data samples. According to the method provided by the invention, the calculation complexity is reduced and the calculation time is shortened while the dimensionality reduction is realized. The invention furthermore provides a local learning feature weight selection-based medical data classification device which also can realize above technical effects.

Description

technical field [0001] The present invention relates to the field of medical diagnosis, more specifically, to a medical data classification method and device based on local learning feature weight selection. Background technique [0002] With the development of artificial intelligence, computer technology has also played an important role in the medical field, realizing artificial intelligence in the medical field. Combining computer technology with a large amount of authoritative knowledge and experience of human medical experts in many fields, a medical diagnosis system has been developed, which can effectively solve various clinical problems and play a role in assisting doctors in diagnosis. [0003] In the medical diagnosis system, DNA microarray technology, that is, gene chip, is introduced. The application of gene chip can quantitatively analyze the level of a large amount of gene expression data at the same time, and through these data, the essence of biology can be s...

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Application Information

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IPC IPC(8): G06F17/30G06F19/00
CPCG06F16/285
Inventor 张莉黄晓娟王邦军张召李凡长
Owner SUZHOU UNIV
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