基于融合特征与优化随机森林的眼动模式识别

Eye movement pattern recognition based on fused features and optimized random forest

为充分挖掘眼动模式信息,最大限度优化模型效果,提高眼动模式识别准确率,本文提出了一种基于融合特征与优化随机森林的眼动模式识别方法。首先提取常规眼动特征、眼动序列子模式特征、视线点高斯分布特征3组特征参数,结合ReliefF选择重要的特征,建立融合特征矩阵,然后以随机森林为基础,使用粒子群算法对模型参数全局寻优,建立优化随机森林眼动模式识别模型。通过自闭症患者眼动实验公开数据集验证本文所提方法的有效性,实验结果表明所提方法能较好区分正常人和自闭症患者之间的眼动模式差异,相较于常规眼动特征随机森林的分类准确率提升了9.57%。因此,融合特征能更好的挖掘眼动模式包含的信息,粒子群算法能有效优化模式识别模型效果,为眼动模式识别提供了新思路与方法。

To fully exploit the eye movement pattern information,maximize the optimization model effect and improve the eye movement pattern recognition accuracy,this paper proposes an eye movement pattern recognition method based on fused features and optimized random forest.First,we extract three groups of feature parameters:Conventional eye movement features,eye movement sequence sub-pattern features,and gaze points gaussian distribution features,combine them with ReliefF to select important features and build a fused features matrix.Then we use the particle swarm algorithm to globally seek the model parameters based on random forest to build the optimized random forest eye movement pattern recognition model.We verify the effectiveness of the proposed method by using the open dataset of eye movement experiments of autistic patients,and the experimental results show that the proposed method can better distinguish the difference of eye movement patterns between normal and autistic patients,and the classification accuracy is improved by 9.57%compared with the random forest of Conventional eye movement features.Therefore,the fused features can better exploit the information contained in the eye movement patterns,and the particle swarm algorithm can effectively optimize the effect of the pattern recognition model,which provides a new idea and method for eye movement pattern recognition.