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Band Priority Index: A Feature Selection Framework for Hyperspectral Imagery

REMOTE SENSING 2018年第7期10卷 1095-1095页

作者： Zhang, Wenqiang Li, Xiaorun Zhao, Liaoying Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Dept Comp Sci Hangzhou 310027 Peoples R China

Hyperspectral Band Selection (BS) aims to select a few informative and distinctive bands to represent the whole image cube. In this paper, an unsupervised BS framework named the band priority index (BPI) is proposed. The basic idea of BPI is to find the bands with large amounts of information and low correlation. Sequential forward search (SFS) is used to avoid an exhaustive search, and the objective function of BPI consist of two parts: the information metric and the correlation metric. We proposed a new band correlation metric, namely, the joint correlation coefficient (JCC), to estimate the joint correlation between a single band and multiple bands. JCC uses the angle between a band and the hyperplane determined by a band set to evaluate the correlation between them. To estimate the amount of information, the variance and entropy are used as the information metric for BPI, respectively. Since BPI is a framework for BS, other information metrics and different mathematic functions of the angle can also be used in the model, which means there are various implementations of BPI. The BPI-based methods have the advantages as follows: (1) The selected bands are informative and distinctive. (2) The BPI-based methods usually have good computational efficiencies. (3) These methods have the potential to determine the number of bands to be selected. The experimental results on different real hyperspectral datasets demonstrate that the BPI-based methods are highly efficient and accurate BS methods.

关键词： feature selection dimensionality reduction hyperspectral remote sensing classification greedy search algorithm

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Computer-Assisted Approaches to Identify Functional Gene Networks Involved in Traumatic Brain Injury

Computer-Assisted Approaches to Identify Functional Gene Net...

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作者： Anthony San Lucas John Redell Pramod Dash Yin Liu

Traumatic brain injury (TBI), a leading cause of death and disability in industrialized countries, is a result of an external force damaging brain tissue, accompanied with delayed pathogenic events which aggravate the injury. Molecular responses to TBI have not been well characterized, leaving molecular classification of TBI cases difficult. TBI subtype classification is an important step towards the development and selective application of novel treatments. To improve TBI classification, we have performed a network-based computational analysis on gene expression profiles from entire rat genome to identify functional gene subnetworks. The gene expression profiles are obtained from two experimental models of injury in rats: the controlled cortical impact (CCI; a focal brain injury) and fluid percussion injury (FPI; a diffuse brain injury). We demonstrate that the analysis of gene subnetworks is more suitable to classify the heterogeneous responses to different TBI models, compared to conventional analysis using an individual gene list. We therefore believe that effectively incorporating gene expression profiles into protein interaction information can improve the identification of functional subnetworks involved in TBI. The systems approach could lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks could have important prognostic functions for patients who sustain mild TBIs. less

关键词： Biomarkers Protein interactions Gene expression profiles Gene networks TBI subtype classification greedy search algorithm

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Network sensor health problem

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TRANSPORTATION REsearch PART C-EMERGING TECHNOLOGIES 2016年 68卷 300-310页

作者： Sun, Zhe Jin, Wen-Long Ng, ManWo Univ Calif Irvine Dept Civil & Environm Engn Calif Inst Telecommun & Informat Technol Inst Transportat Studies 4038 Anteater Instruct & Res Bldg Irvine CA 92697 USA Univ Calif Irvine Dept Civil & Environm Engn Inst Transportat Studies 4191 Anteater Instruct & Res Bldg Irvine CA 92697 USA Old Dominion Univ Dept Informat Technol & Decis Sci Strome Coll Business 2165 Constant Hall Norfolk VA 23529 USA

Many existing studies on the sensor health problem determine an individual sensor's health status based on the statistical characteristics of collected data by the sensor. In this research, we study the sensor health problem at the network level, which is referred to as the network sensor health problem. First, based on the conservation principle of daily flows in a network, we separate all links into base links and non-base links, such that the flows on the latter can be calculated from those on the former. In reality, the network flow conservation principle can be violated due to the existence of unhealthy sensors. Then we define the least inconsistent base set of links as those that minimize the sum of squares of the differences between observed and calculated flows on non-base links. But such least inconsistent base sets may not be unique in a general road network. Finally we define the health index of an individual sensor as the frequency that it appears in all of the least inconsistent base sets. Intuitively, a lower health index suggests that the corresponding sensor is more likely to be unhealthy. We present the brute force method to find all least inconsistent base sets and calculate the health indices. We also propose a greedy search algorithm to calculate the approximate health indices more efficiently. We solve the network sensor health problem for a real-world example with 16 nodes and 30 links, among which 18 links are monitored with loop detectors. Using daily traffic count data from the Caltrans Performance Measurement System (PeMS) database, we use both the brute-force and greedy search methods to calculate the health indices for all the sensors. We find that all the four sensors flagged as unhealthy (high value) by PeMS have the lowest health indices. This confirms that a sensor with a lower health index is more likely to be unhealthy. Therefore, we can use such health indices to determine the relative reliability of different sensors' data and pri

关键词： Network flow conservation Least inconsistent base set Health index greedy search algorithm Caltrans Performance Measurement System (PeMS) database

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Synchronization of Replenishment Decisions for the Multi Stage Economic Lot Delivery and Scheduling Problem 6

Synchronization of Replenishment Decisions for the Multi Sta...

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6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)

作者： As'ad, Rami Afif Bahroun, Zied Amer Univ Sharjah Dept Ind Engn POB 26666 Sharjah U Arab Emirates

ISBN: (纸本)9781467366014

The challenging problem of jointly sequencing multiple items at the manufacturer facility, deciding on the delivery policy of end-items to the downstream retailers, as well as the replenishment strategy of raw materials from upstream suppliers is tackled in this paper. The objective is to develop the production and shipping schedule such that the chain-wide total setup, holding and ordering costs are minimized while satisfying end customers' demand. The manufacturer adopts the common rotation cycle policy in which all the end-items are produced sequentially on a single facility within that cycle. A mixed-integer nonlinear programming formulation that adopts the integer multiplier of the cycle time policy is developed. Through the use of differential calculus, an interval that brackets the optimal value of the cycle time is identified followed by a greedy search algorithm within that interval. A numerical example that illustrates the efficiency of the proposed solution algorithm is also presented.

关键词： Economic lot and delivery scheduling problem integer multiplier policy greedy search algorithm

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Reconstruction of Gene Regulatory Network Based on Bayesian Network

Reconstruction of Gene Regulatory Network Based on Bayesian ...

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2015 International Conference on Advanced Educational Technology and Information Engineering(AETIE 2015)

作者： B.Yu M.H.Wang L.K.Zhao College of Mathematics and Physics Qingdao University of Science and Technology

Gene regulatory network, which focuses on the complex interactions of genes in 1ife, is an important part in the study of the functiona1 genomics and the frontier of bioinformatics and systems biology research. Bayesian network is powerfu1 tools to genetic networks research. As for the empirical analysis, a subset consisting of 15 genes is chosen from yeast gene expressing data as training data, we first use greedy search(GS) algorithm to get the priori information for the K2 algorithm, and then apply the K2 method to the dataset of gene expression profile to get the topological structure of the Bayesian networks, thus inferring the interaction between genes. We verify the effectiveness of our approach by consulting the corresponding experiment articles, and reduce the computational time in learning network, finally get more stable structures.

关键词： Bayesian network gene regulatory network greedy search algorithm K2 algorithm

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Assessing Project Portfolio Risk Based on Bayesian Network

Assessing Project Portfolio Risk Based on Bayesian Network

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Guan, Dujuan Hipel, Keith W. Fang, Liping Guo, Peng Northwestern Polytech Univ Sch Management Xian 710072 Peoples R China Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Ryerson Univ Dept Mech & Ind Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9781479938407

The risk of a project portfolio is assessed using a new methodology that identifies the risk transfer in projects by using a Bayesian network structure learning algorithm to construct an interdependent network of risks. In particular, to overcome the drawback of a greedy search algorithm having a random starting structure, the mutual information between project risks is measured before executing the algorithm by computing the impact of technical interactions on project risks. It is demonstrated that the preprocessing can exclude the error and indistinctive connections, so as to reduce the search space effectively. Finally, the project portfolio risk of a practical case is assessed using this method, and the results show that the interdependent network of risks is an effective tool to reveal the risk transfer in projects and infer the value of project portfolio risk.

关键词： project portfolio Bayesian network technical interactions mutual information greedy search algorithm

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Assessing Project Portfolio Risk Based on Bayesian Network

Assessing Project Portfolio Risk Based on Bayesian Network

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Dujuan Guan Keith W. Hipel Liping Fang Peng Guo School of Management Northwestern Polytechnical University Department of Systems Design Engineering University of Waterloo Department of Mechanical and Industrial Engineering Ryerson University

ISBN: (纸本)9781479938414

关键词： Project portfolio Bayesian network Technical interactions Mutual information greedy search algorithm

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Online selection of the best k-feature subset for object tracking

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2012年第2期23卷 254-263页

作者： Li, Guorong Huang, Qingming Pang, Junbiao Jiang, Shuqiang Qin, Lei Chinese Acad Sci Grad Univ Beijing 100190 Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing 100080 Peoples R China

In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishable subset for tracking. In the paper, we use a special tree to formalize the feature subset space. Then conditional entropy is used to evaluating feature subset and a simple but efficient greedy search algorithm is developed to search this tree to obtain the optimal k-feature subset quickly. Furthermore, our online k-feature subset selection method is integrated into particle filter for robust tracking. Extensive experiments demonstrate that k-feature subset selected by our method is more discriminative and thus can improve tracking performance considerably. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Object tracking Feature subset selection Feature selection Feature subset tree Conditional entropy greedy search algorithm Particle filter Online selection

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A Fast Signal Timing algorithm for Individual Oversaturated Intersections

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2011年第1期12卷 280-283页

作者： Zhao, Lei Peng, Xiaoshan Li, Li Li, Zhenjiang Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Tsinghua Univ Dept Automat TNList Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat Key Lab Complex Syst & Intelligence Sci Beijing 100080 Peoples R China

In this paper, we propose a fast greedy search algorithm for optimal single-cycle signal timing at individual oversaturated intersections. We illustrate the efficiency of the algorithm with a numerical example in the ... 详细信息

关键词： greedy search algorithm oversaturated intersection signal timing

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Joint Bandwidth and Power Allocation With Admission Control in Wireless Multi-User Networks With and Without Relaying

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2011年第4期59卷 1801-1813页

作者： Gong, Xiaowen Vorobyov, Sergiy A. Tellambura, Chintha Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

Equal allocation of bandwidth and/or power may not be efficient for wireless multi-user networks with limited bandwidth and power resources. Optimal joint bandwidth and power allocation strategies for wireless multi-user networks with and without relaying are proposed in this paper for 1) the maximization of the sum capacity of all users;2) the maximization of the worst user capacity;and 3) the minimization of the total power consumption of all users. It is shown that the proposed allocation problems are convex and, therefore, can be solved efficiently. Moreover, joint bandwidth and power allocation for admission control is considered. A suboptimal greedy search algorithm is developed to solve the admission control problem efficiently. Instructive analysis of the greedy search shows that it can achieve good performance, and the condition under which the greedy search is optimal is derived. The formal and in-depth analysis of the greedy search algorithm presented in this paper can serve as a benchmark for analyzing similar algorithms in other applications. The performance improvements offered by the proposed optimal joint bandwidth and power allocation are demonstrated by simulations. The advantages of the suboptimal greedy search algorithm for admission control are also shown in numerical results.

关键词： Admission control greedy search algorithm joint bandwidth and power allocation wireless multi-user networks

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