Biological age step-by-step predication method based on support vector machine

A technology of support vector machine and prediction method, which is applied to computer parts, instruments, characters and pattern recognition, etc. It can solve the problems of cumbersome and complicated methods, low prediction efficiency and low accuracy, and achieves high classification accuracy and wide promotion. Application ability, the effect of improving accuracy

Inactive Publication Date: 2015-10-07
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problems of low prediction efficiency, low accuracy, high cost and complex methods

Method used

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  • Biological age step-by-step predication method based on support vector machine
  • Biological age step-by-step predication method based on support vector machine
  • Biological age step-by-step predication method based on support vector machine

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Experimental program
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specific Embodiment approach 1

[0029] Specific implementation mode one: combine figure 1 Description of this embodiment, a step-by-step prediction method of biological age based on support vector machine, is characterized in that, a step-by-step prediction method of biological age based on support vector machine is specifically carried out according to the following steps:

[0030] Step 1. Organize the physical property data of the biological samples obtained in the experiment to make a biological age data set (in the form of MS Excel, Notepad or ASCII coded file);

[0031] Classify the detected physical attribute data of biological samples, and store the different physical attribute data of each biological sample in a row in the matrix, that is, the different physical attribute data of a biological sample correspond to a row vector, and different biological samples are put together to form a matrix;

[0032] Wherein, the physical attribute data includes gender, length, diameter, height, total weight, weig...

specific Embodiment approach 2

[0045] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the combined training set obtained according to step 3 in step 4 uses a support vector machine classifier for training to generate a corresponding support vector machine model; specifically The process is:

[0046] Use the support vector machine classifier to train the merged training set obtained in step 3, and before generating the corresponding merged support vector machine model, first normalize the merged training set data, and map all the data To the pre-set value range, and then use the same mapping method to process the test set data;

[0047] The role of the normalization algorithm is: 1. Each attribute in the data set has an actual physical background, so their units and ranges are different. Normalization can eliminate the influence of units or orders of magnitude, and map all data to a predetermined range, which facilitates subsequent data processing; 2. Normaliz...

specific Embodiment approach 3

[0087]Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that in the step five, the parameter optimization algorithm is used to optimize the penalty parameter C and the parameter γ in the RBF kernel function during the establishment of the support vector machine model, Establish the optimal support vector machine model according to the optimization results; the specific process is:

[0088] Parameter optimization algorithms include grid optimization algorithm, genetic algorithm and particle swarm optimization algorithm;

[0089] Grid search algorithm:

[0090] (1) Use the grid search method to find the optimal combination of the penalty parameter C of the optimal support vector machine model and the parameter γ of the support vector machine RBF kernel function;

[0091] The search is a two-step process:

[0092] The first step is a rough search, and the second step is a fine search;

[0093] In the first step of the searc...

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Abstract

The invention discloses a biological age step-by-step predication method based on a support vector machine, and relates to the biological age step-by-step predication method based on the support vector machine. The invention aims to solve the problems that a conventional biological age predication method is low in predication efficiency, low in accuracy, high in cost, and complex. According to the technical scheme, the biological age step-by-step predication method comprises: step I, preparing a biological age data set; step II, distinguishing a biological sample with known ages from a biological sample with unknown ages; step III, carrying out inter-group classifying; step IV, generating a corresponding support vector machine model; step V, establishing an optimal support vector machine model; step VI, establishing an optical characteristic sub set; step VII, obtaining the group type of an age group corresponding to the biological sample with known ages in the test set; step VIII, carrying out inter-group classifying; step IX, generating an inter-group classified support vector machine model; and step X, obtaining the exact age of a test set sample in certain age group. The biological age step-by-step predication method is applied to the biological age predication field.

Description

technical field [0001] The invention relates to a step-by-step prediction method of biological age based on a support vector machine. Background technique [0002] Age prediction is an essential part of farming, veterinary medicine, and even rare animal research. Accurately predicting the age of organisms can help relevant medical personnel formulate medical plans for organisms more rationally and scientifically, and match the dosage of drugs, so as to further improve the treatment effect. More generally, the biological age prediction method of systems science provides convenience for the comprehensive study of the characteristics of populations. However, traditional biological age prediction methods often require a large number of systematic experiments and a certain amount of work experience to determine the age of biological individuals, resulting in low efficiency, low accuracy, high cost, and cumbersome and complicated methods for biological age prediction. The tradit...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 尹珅田洋高会军
Owner HARBIN INST OF TECH
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