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Multi-sensor detected object classification method based on
support vector machine |
| LI Kan1, HUANG Wen-xiong1, HUANG Zhong-hua2 |
1. School of Computer, Beijing Institute of Technology, Beijing 100081, China;2. School of Mechano-Electronics
Engineering, Beijing Institute of Technology, Beijing 100081, China |
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Abstract Multi-sensor detected data often have noise. The current multiple classification algorithms are susceptible to noise interference, have weak fault-tolerance, and can lead to data misclassification. The multi-sensor detected object classification method was proposed in order to solve the problems. Noise-tolerance least squares projection twin support vector machine (NLSPTSVM) was presented in order to remove outliers to improve noise-tolerance. NLSPTSVM with confidence-degree,based on the defined confidence-degree of NLSPTSVM and the minimal hypersphere distance, was used as binary classifier, and advanced the noise reduction process before the generation of directed graph, according to the idea that “the upper classification performance has more effects on the generalization performance of classification model”. A high accuracy, noise-tolerance and fault-tolerance multiple classification support vector machine was proposed, called noise-tolerance up-preferred multiple directed acyclic graph support vector machines (NUMDAG-SVMs). Experiments were conducted to test the performance of the algorithm. Experimental results in public datasets indicate that our NUMDAG-SVMs have comparable classification accuracy, better noise-tolerance and fault-tolerance to those other algorithms. The algorithm can get good classification performance in sensor data．
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Published: 01 January 2013
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基于支持向量机的多传感器探测目标分类方法
针对传感器探测的数据常含有噪声,分类算法易受噪声数据干扰、容错能力差而产生错分问题,研究对多传感器探测目标进行分类的方法.提出容噪最小二乘投影双支持向量机(NLSPTSVM),去除离群点,提高容噪性能；通过定义NLSPTSVM置信度,以样本的最小超球体距为依据,根据“越是上层分类器的分类性能对分类模型的推广性能影响越大”的思想,以置信度NLSPTSVM作为二分类器,将NLSPTSVM的降噪过程提前到生成有向图之前,提出分类精度高、容噪性和容错性强的多分类支持向量机——容噪上层择优多路支持向量机(NUMDAG-SVMs).实验表明,NUMDAG-SVMs与同类算法相比具有更优的分类准确率和更强的容噪性和容错性.采用NUMDAG-SVMs对传感器采集的真实数据进行分类,取得了很好的结果.
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