特征选择
模式识别(心理学)
人工智能
k-最近邻算法
分类器(UML)
计算机科学
特征(语言学)
融合
降维
数据挖掘
语言学
哲学
作者
Changjian Deng,Kun Lv,Debo Shi,Bo Yang,Song Yu,Zhiyi He,Yan Jia
出处
期刊:Sensors
[MDPI AG]
日期:2018-06-12
卷期号:18 (6): 1909-1909
被引量:24
摘要
In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.
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