Ensemble classification method based on randomized greedy feature selection

Abstract

The invention discloses an ensemble classification method based on randomized greedy feature selection, and belongs to the field of bioinformatics and data mining. The method is used for classifying gene expression data related to plant stress response. The method includes the following steps that 1, randomness is introduced into a traditional greedy algorithm to conduct feature selection; 2, a weighting local modular function serving as a community discovery evaluation index in a complex network is used as heuristic information of the randomized greedy algorithm; 3, base classifiers are trained in each feature subset with a support vector machine algorithm; 4, clustering partition is conducted on the base classifiers with an affinity propagation clustering algorithm; 5, base classifiers serving as class representative points in the cluster are used for conducting integration, and an ensemble classification model is formed with a simple majority voting method. By means of the method, whether plant samples are stressed or not can be recognized according to gene expression data, and the microarray data classification precision is greatly improved; besides, the algorithm is high in generalization capability and has very high stability.

Description

technical field [0001] The invention belongs to the fields of bioinformatics and data mining, and in particular relates to the selection of important genes of gene expression data and the construction of a selective integrated classification model. Background technique [0002] The development of high-throughput sequencing technology provides researchers with massive gene expression data, and extracting valuable information from it has become a research hotspot in bioinformatics. Plants are often affected by diseases, insect pests and environmental factors during the growth process. How to predict and do a good job in prevention and control will play a very important role in the development of forestry, agriculture, animal husbandry, environmental protection and other aspects. Due to the characteristics of "high dimensionality", "small sample" and "high redundancy" of gene expression data, the traditional single classification algorithm will have problems such as poor classi...

AI Technical Summary

Problems solved by technology

[0006] In the case of a large number of base classifiers, there will be some redundant classifiers, resulting in poor overall differentiation

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