Gene classification method and system based on clustering and random forest algorithms

Abstract

The invention relates to a gene classification method and system based on clustering and random forest algorithms and belongs to the technical field of biological information. The method comprises a step of acquiring gene sample data, clustering the acquired gene sample data by using the clustering algorithm to obtain a cluster center, and supplementing a training sample set with an obtained cluster center set, a step of adjusting the number of fixed decision tree random description attributes in a traditional random forest algorithm to a random value, wherein on one hand, strong decision trees in a decision tree set are kept, on the other hand, the number of average random description attributes of the decision tree set is reduced, thus the correlation between the decision trees is further reduced, and a step of predicting genetic data to be classified by using each decision tree in a random forest model. According to the method and the system, the cluster center obtained through theclustering algorithm is taken as artificial data to expand the training set of the random forest model, thus the random forest model is fully trained, the obtained classification model has high precision, and the accuracy of the classification of genetic data is improved.

Description

technical field [0001] The invention relates to a gene classification method and system based on clustering and random forest algorithms, belonging to the technical field of biological information. Background technique [0002] Genetic data classification is a research focus and hot issue in the field of information and decision-making at present, because genetic data has the characteristics of high dimensionality, small sample size, high noise, and many redundant attributes. Using traditional data analysis methods may face problems such as long time-consuming and low classification accuracy. In view of these characteristics of genetic data, existing pattern recognition and machine learning algorithms, such as linear discriminant, support vector machine, artificial neural network and k-nearest neighbor algorithms, have been widely researched and applied. [0003] Random Forest algorithm (Random Forest, RF) is a kind of integrated learning algorithm, which has the characteri...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a gene classification method and system based on clustering and random forest algorithm, to solve the problem of inaccurate classification due to less sample data in current gene classification methods

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