College of Electrical & Electronic Engineering, Wenzhou University, Wenzhou 325035, China.
Department of Planning & Finance, Wenzhou University, Wenzhou 325035, China.
Sensors (Basel). 2020 Mar 10;20(5):1528. doi: 10.3390/s20051528.
A novel multi-classification method, which integrates the elastic net and probabilistic support vector machine, was proposed to solve this problem in cancer detection with gene expression profile data of platelets, whose problems mainly are a kind of multi-class classification problem with high dimension, small samples, and collinear data. The strategy of one-against-all (OVA) was employed to decompose the multi-classification problem into a series of binary classification problems. The elastic net was used to select class-specific features for the binary classification problems, and the probabilistic support vector machine was used to make the outputs of the binary classifiers with class-specific features comparable. Simulation data and gene expression profile data were intended to verify the effectiveness of the proposed method. Results indicate that the proposed method can automatically select class-specific features and obtain better performance of classification than that of the conventional multi-class classification methods, which are mainly based on global feature selection methods. This study indicates the proposed method is suitable for general multi-classification problems featured with high-dimension, small samples, and collinear data.
提出了一种新的多分类方法，将弹性网络和概率支持向量机相结合，用于解决血小板基因表达谱数据中癌症检测的问题，其主要问题是一种具有高维、小样本和共线性数据的多分类问题。采用一对多（OVA）策略将多分类问题分解为一系列二分类问题。弹性网络用于为二分类问题选择特定于类的特征，概率支持向量机用于使具有特定于类的特征的二分类器的输出具有可比性。模拟数据和基因表达谱数据旨在验证所提出方法的有效性。结果表明，与主要基于全局特征选择方法的传统多分类方法相比，所提出的方法可以自动选择特定于类的特征，并获得更好的分类性能。本研究表明，所提出的方法适用于具有高维、小样本和共线性数据的一般多分类问题。
