改进鲸鱼算法寻优支持向量机的眼动数据分类研究

Research on eye movement data classification using support vector machine with improved whale optimization algorithm

支持向量机在进行不同眼动模式分类任务时受参数影响较大,针对这一问题,本文提出一种基于改进鲸鱼算法优化支持向量机的算法以提升眼动数据分类性能。根据眼动数据特点,本研究先提取注视、眼跳相关的57个特征,再利用近邻相关(ReliefF)算法进行特征筛选。针对鲸鱼算法收敛精度低,易陷入局部最小值等问题,本文引入惯性权重平衡局部搜索和全局搜索,加快算法收敛速度,同时利用差分变异策略增加个体多样性,跳出局部最优。本文对8个测试函数进行实验,结果表明改进鲸鱼算法具有最佳的收敛精度和收敛速度。最后,本文将改进鲸鱼算法优化支持向量机模型应用于自闭症眼动数据分类任务,公开数据集实验结果表明,相较于传统的支持向量机方法,本文方法的眼动数据分类准确率有着较大提升,相较于标准鲸鱼算法和其他优化算法,本文方法优化后的模型具有更高的分类精度,为眼动模式识别提供了新思路与方法。未来或可利用眼动仪获取的眼动数据,结合本文方法辅助医疗诊断。

When performing eye movement pattern classification for different tasks,support vector machines are greatly affected by parameters.To address this problem,we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification.According to the characteristics of eye movement data,this study first extracts 57 features related to fixation and saccade,then uses the ReliefF algorithm for feature selection.To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm,we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum.In this paper,experiments are conducted on eight test functions,and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed.Finally,this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism,and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method.Compared with the standard whale algorithm and other optimization algorithms,the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition.In the future,eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.