The Experts below are selected from a list of 93 Experts worldwide ranked by ideXlab platform
Xuegong Zhang - One of the best experts on this subject based on the ideXlab platform.
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a simple strategy for Detecting Outlier samples in microarray data
International Conference on Control Automation Robotics and Vision, 2004Co-Authors: Xuegong ZhangAbstract:Microarrays can monitor expression levels of thousands of genes simultaneously. Many people have used the gene expression data obtained with microarrays to classify different groups of samples, such as different types or subtypes of cancers. In our experiments as well as those of some other investigators, it has been observed that in some microarray data sets, there might be Outlier samples which are either caused by imperfectness in the experiments or by possible mislabeling at certain steps. The existence of such samples impacts classification accuracy and may even cause misleading conclusions. In this paper, we studied this problem with two simulated data sets of typical scenarios and formed a simple but powerful strategy for Detecting such Outlier or mislabeled samples, built upon cross validation of the basic SVM classifier. The strategy was applied to a public colon cancer data set and it successfully detected 6 Outlier cases. This work suggests an effective scheme for Detecting Outlier samples in a data set and for evaluating the sample quality.
Yunhao Liu - One of the best experts on this subject based on the ideXlab platform.
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Detecting Outlier measurements based on graph rigidity for wireless sensor network localization
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Zheng Yang, Tao Chen, Yiyang Zhao, Wei Gong, Yunhao LiuAbstract:A majority of localization approaches for wireless sensor networks rely on the measurements of internode distance. Errors are inevitable in distance measurements, and we observe that a small number of Outliers can drastically degrade localization accuracy. To deal with noisy and Outlier ranging results, a straightforward method, known as triangle inequality, has often been employed in previous studies. However, triangle inequality has its own limitations that make it far from accurate and reliable. In this paper, we first analyze how much information is needed to identify Outlier measurements. Applying the rigidity theory, we propose the concept of verifiable edges and derive the conditions for an edge to be verifiable. On this basis, we design a localization approach with Outlier detection, which explicitly eliminates ranges with large errors before location computation. Considering the entire network, we define verifiable graphs in which all edges are verifiable. If a wireless network meets the requirements of graph verifiability, it is not only localizable but Outlier resistant as well. Extensive simulations are conducted to examine the effectiveness of the proposed approach. The results show remarkable improvement in location accuracy by sifting Outliers.
Junyi Shen - One of the best experts on this subject based on the ideXlab platform.
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Detecting Outlier samples in multivariate time series dataset
Knowledge Based Systems, 2008Co-Authors: Xiaoqing Weng, Junyi ShenAbstract:Multivariate time series (MTS) samples which differ significantly from other MTS samples are referred to as Outlier samples. In this paper, an algorithm designed to efficiently detect the top n Outlier samples in MTS dataset, based on Solving Set, is proposed. An extended Frobenius Norm is used to compute the distance between MTS samples. The Outlier score of MTS sample is the sum of the distances from its k nearest neighbors. The time complexity of the algorithm is subquadratic. We conduct experiments on two real-world datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experiment results show the efficiency and effectiveness of the algorithm.
Veska Georgieva - One of the best experts on this subject based on the ideXlab platform.
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Detecting Outlier Intelligence in the behavior of intelligent coalitions of agents
2017 IEEE Congress on Evolutionary Computation (CEC), 2017Co-Authors: Laszlo Barna Iantovics, Adrian Gligor, Veska GeorgievaAbstract:In our research, we consider the measuring of machine intelligence based on the intelligence (ability to solve various tasks in high efficiency, with a grade of flexibility and robustness) in solving difficult problems/tasks (NP-hard and/or have different types of uncertainties). An intelligent cooperative coalition of agents (could be a whole cooperative multiagent system or a part of it) have variability in the problem-solving intelligence. It is able to solve more or less intelligently different problems. For some problems solving it could manifest even extremely low or extremely high intelligence. We call such intelligence values as Outlier intelligence, which could be low Outlier intelligence values or high Outlier intelligence values. In this paper, we propose a novel method called OutIntDet (Outlier Intelligence Detection Method) for Detecting Outlier intelligence values. In order to sustain the effectiveness of the proposed method, a case study where we considered a coalition of agents which solve an NP-hard problem was performed. OutIntDet could be useful to be implemented in some intelligence metrics that are based on measuring problems-solving intelligence, being able to detect low and high Outlier intelligence. This could make the metrics more accurate and robust. OutIntDet is also appropriate for the identification of the problems for whose solving the coalition manifest very low or very high intelligence. There is also presented an appropriate calculation of the MIQ (Machine Intelligence Quotient) based on the properties of measured problem-solving intelligence data.
Zheng Yang - One of the best experts on this subject based on the ideXlab platform.
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Detecting Outlier measurements based on graph rigidity for wireless sensor network localization
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Zheng Yang, Tao Chen, Yiyang Zhao, Wei Gong, Yunhao LiuAbstract:A majority of localization approaches for wireless sensor networks rely on the measurements of internode distance. Errors are inevitable in distance measurements, and we observe that a small number of Outliers can drastically degrade localization accuracy. To deal with noisy and Outlier ranging results, a straightforward method, known as triangle inequality, has often been employed in previous studies. However, triangle inequality has its own limitations that make it far from accurate and reliable. In this paper, we first analyze how much information is needed to identify Outlier measurements. Applying the rigidity theory, we propose the concept of verifiable edges and derive the conditions for an edge to be verifiable. On this basis, we design a localization approach with Outlier detection, which explicitly eliminates ranges with large errors before location computation. Considering the entire network, we define verifiable graphs in which all edges are verifiable. If a wireless network meets the requirements of graph verifiability, it is not only localizable but Outlier resistant as well. Extensive simulations are conducted to examine the effectiveness of the proposed approach. The results show remarkable improvement in location accuracy by sifting Outliers.