Label Distribution Learning for Computer Aided Diagnosis of Multi-class ASD Classification

Abstract

Autism spectrum disorder (ASD) is a series of complex neurodevelopmental disorders, including several diseases related to developmental disorders, but most of the existing diagnosis methods for autism are binary classification methods which cannot meet the actual needs. In addition, the label noise contained in ASD data, as well as the characteristics of high dimensionality and data imbalance, has brought great challenges to traditional methods. To this end, a new computer aided diagnosis method of ASD is proposed. This method solves the label noise by introducing label distribution learning (LDL), introduces a cost-sensitive mechanism to solve the data imbalance, uses label distribution support vector regression (SVR) to solve the classification difficulties caused by high-dimensional features by mapping samples to the feature space, and finally realizes the computer aided diagnosis of multi-class ASD. Experimental results show that compared with the existing methods, the proposed method overcomes the imbalance of the influence of the majority class and the minority class on the results, can effectively solve the class imbalance in ASD diagnosis, and has better and stable classification performance, which can assist in the diagnosis of ASD.

Keywords

• |computer aided diagnosis of autism spectrum disorder (asd)|cost-sensitive mechanism|label distribution learning (ldl)|support vector regression (svr)