Multiclass unbalanced genomics data iterative integrated feature selection method and system

Abstract

The present invention discloses a multiclass unbalanced genomics data iterative integrated feature selection method and system. Aiming at the characteristic of unbalanced data distribution of multi-labeled genomics data, the present invention provides the iterative feature selection method. On the basis of integrating classifiers in a one-to-many manner, undersampling or oversampling and feature selection are iteratively operated, so that samples of a data set gradually reach a balanced state along with gradual decrease of the number of features. By adopting a classifier obtained after integration in the process, classification identification capability on subclass samples can be obviously improved. A weak classifier based on sub balanced data training is integrated into a strong classifier by adopting an integrated learning technology, so that classification accuracy can be obviously improved.

Description

technical field [0001] The invention relates to the field of feature selection and recognition, in particular to a feature selection method and system for iterative integration of multi-class unbalanced genomics data. Background technique [0002] Genomic microarray technology has been widely used in cancer diagnosis, but the identification and determination of cancer-related genes remains a major challenge. Genomic microarray data usually has tens of thousands of genes, and discovering potential markers or gene sets related to cancer among these genes is a very important task. If this type of gene selection problem is analyzed from the perspective of machine learning, it can be regarded as a feature selection problem. The goal of feature selection is to identify features that are highly correlated with class labels. [0003] According to whether learning methods are used to evaluate feature subsets, feature selection methods can be divided into three main categories: (1) ...

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Problems solved by technology

[0009] In view of the deficiencies in the prior art above, the purpose of the present invention is to provide a feature selection method and system for iterative integration of multi-class unbalanced genomics data, aiming to solve the problem of low classification accuracy of the existing feature selection methods for predicting small class samples, Issues such as features that are highly correlated with small class labels are ignored

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