Feature selection method and device

A feature selection method and feature orientation technology, applied in the field of medical diagnosis, can solve the problems of poor promotion ability, high calculation and payment, and low learning efficiency.

Active Publication Date: 2014-07-16
SUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. Filter (filtering) method, the feature selection process of this method has nothing to do with the learning algorithm, it is to estimate the effectiveness of a certain feature subset through the value of an adaptive function, and has nothing to do with the specific classifier, although this method can be used independently learning algorithm, but its learning efficiency is not high
[0005] 2. Wrapper (encapsulation) meth

Method used

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0055] figure 1 This is a flowchart of a feature selection method provided in Embodiment 1 of the present application.

[0056] like figure 1 As shown, the method includes:

[0057] Step A: In response to the received training sample set including a plurality of training samples with the same characteristics, according to the category of the training samples in the training sample set, the training sample set is divided into a first training sample set and a second training sample set, and according to the training sample set The features of the training samples in the sample set are used to generate a first feature index set corresponding to the first training sample set, and a second feature index set corresponding to the second training sample set.

[0058] In the embodiment of the present application, firstly, in response to the received training sample set, the training sample set may be input by the user through import, or may be input by manual input, and the training...

Embodiment 2

[0107] figure 2 This is a schematic structural diagram of a feature selection apparatus provided in Embodiment 2 of the present application.

[0108] like figure 2 As shown, the device includes:

[0109] Response unit 1, for performing step A in response to the received training sample set including a plurality of training samples with the same characteristics, and dividing the training sample set into a first training sample set and a second training sample set according to the categories of the training samples in the training sample set A sample set is generated, and a first feature index set corresponding to the first training sample set and a second feature index set corresponding to the second training sample set are generated according to the features of the training samples in the training sample set.

[0110] The statistics unit 2 is connected with the response unit 1, and is used for performing step B of counting the sum of the number of features corresponding to...

Embodiment 3

[0132] The embodiment of the present application mainly uses the diagnosis module to verify the result of the feature selection of the present application, and further illustrates the learning efficiency of the feature selection result of the embodiment of the present application.

[0133] In the embodiment of the present application, after the feature selection is completed and the feature index set F is obtained, the feature index set F includes r' elements, and because the training sample set The training sample set after feature selection is determined according to the elements in the feature index set F. Therefore, the training sample set determined according to the feature index set F is: { x ‾ t , y t } i = 1 ...

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Abstract

The invention provides a feature selection method and device. The method includes the first step of generating a first training sample set, a first feature index set corresponding to the first training sample set, a second training sample set and a second feature index set corresponding to the second training sample set through response to received training sample sets, the second step of calculating a first element according to the first training sample set and completing updating of the first feature index set, the third step of calculating a second element according to the second training sample set and completing updating of the second feature index set, and the fourth step of calculating a feature index set according to the obtained first feature index set and the obtained second feature index set when the sum of the number of all features in the updated first feature index set and the number of all features in the updated second feature index set reaches a preset value, and then completing selection of the features. Consequently, withholding calculation is reduced, and the generalization capability is improved in the feature selection process on the basis of ensuring the learning efficiency.

Description

technical field [0001] The present application relates to the technical field of medical diagnosis, and in particular, to a feature selection method and device. Background technique [0002] In today's society, any field is inseparable from the help of computers. The same is true in the field of medical diagnosis. It uses some technologies to simulate the diagnosis and treatment of diseases by medical experts, which can effectively solve various clinical problems and play the role of "doctor's assistant", especially to help young and inexperienced doctors improve their diagnostic skills. , optimize the diagnosis and treatment plan. The application of machine learning in medical diagnosis is from the most primitive application of prior knowledge for heuristic reasoning, to the later probability calculation method and artificial intelligence method, and then to the widely used neural network technology, simulation technology and genetic algorithm, etc. Learning is increasing...

Claims

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Application Information

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IPC IPC(8): G06K9/66
Inventor 张莉曹晋卢星凝王邦军何书萍杨季文李凡长
Owner SUZHOU UNIV
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