燕山大学学报Issue(5)：432-439,8.DOI:10.3969/j.issn.1007-791X.2014.05.010
基于F-score的大数据公共空间模式选择方法
F-score based common spatial pattern selection approach for big data processing
摘要
Abstract
Due to such advantages like simplicity and high speed, the common spatial pattern (CSP) analysis method has been widely applied in various big data processing applications such as information mining, brain signal processing, etc. Facing the human cognitive state recognition problem, a F-score based hybrid feature evaluation and selection method is investigated for CSP discovery and construction. Since F-score may easily and quickly to pick up the effective features from high dimensional data, the proposed F-score based method quantitatively represents the importance of each data pattern. Other solutions are also proposed to conquer the conventional F-score problems like threshold definition difficulty, Information redundancy, lack of automatic and self-adaptive, etc. In the cognitive state analysis experiments, the proposed method obtained a recognition accuracy of 92%on 5 kinds of cognitive tasks, proving it to be a nouvelle and powerful tool for public pattern mining from big data.关键词
公共空间模式/信息选择/F-score/认知状态识别/大数据Key words
common spatial pattern/information filtering/F-score/cognitive state recognition/big data分类
信息技术与安全科学引用本文复制引用
王欣杰,李海峰,马琳,吴明权..基于F-score的大数据公共空间模式选择方法[J].燕山大学学报,2014,(5):432-439,8.基金项目
国家自然科学基金资助项目（61171186，61271345）；语言语音教育部-微软重点实验室开放基金资助项目（HIT. KLOF.20110XX）；中央高校基本科研业务费专项资金资助项目 （）
