Eye tracking data guided feature selection for image classification

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• 作者：Xuan Zhou^a ; ¹Author Vitae ; Xin Gao^b ; ¹Author Vitae ; Jiajun Wang^a ; ^{jjwang@suda.edu.cn}Author Vitae ; Hui Yu^aAuthor Vitae ; Zhiyong Wang^cAuthor Vitae ; Zheru Chi^d ; ^eAuthor Vitae
• 关键词：Eye tracking ; Feature selection ; Quantum genetic algorithm (QGA) ; mRMR ; SVM-RFE
• 刊名：Pattern Recognition
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：56-70
• 全文大小：3638 K
• 卷排序：63

文摘

Proposing to leverage the value of eye tracking data for image classification. Considering the fitness value and generation number in the rotation angle of quantum genetic algorithm. Sequentially implementing mRMR and SVM-RFE method and incorporating the ranking information of the mRMR selector in the ranking procedure of the SVM-RFE.