Time Series Classification Diagnosis Model based on Partical Swarm Optimization and Support Vector Machine

Abstract:

This paper presents a fMRI timeseries classification diagnosis model based on particle swarm optimizationsupport vector machine (PSOSVM), which achieves more accurate judgments and distinctions between patients and healthy individuals by deeply analyzing multidimensional timeseries data of brain regions. This approach is significantly different from the other existing related research work in four aspects as follows: Constructing the feature representation for multidimensional timeseries data of brain regions based on the autoregressive (AR) model; Constructing the classification scheme for multidimensional timeseries of brain regions based on the support vector machine (SVM) model; Constructing the parameter optimization strategy for the classification learning based on the particle swarm optimization (PSO) algorithm; Constructing the classification diagnosis framework for fMRI timeseries data by integrating the above feature representation, optimized classification and parameter optimization patterns. With the mental depression disorder (MDD) as a specific case of empirical analysis, our classification diagnosis model has obtained very positive numerical computation results.

Key words:fMRI multidimensional time series; classification diagnosis; autoregressive (AR) model; support vector machine (SVM); particle swarm optimization (PSO)