Feature extraction and dimensionality reduction by genetic programming based on the Fisher criterion

Abstract: Feature extraction helps to maximize the useful information within a feature vector, by reducing the dimensionality and making the classification effective and simple. In this paper, a novel feature extraction method is proposed: genetic programming (GP) is used to discover features, while the Fisher criterion is employed to assign fitness values. This produces non‐linear features for both two‐class and multiclass recognition, reflecting the discriminating information between classes. Compared with other GP‐based methods which need to generate c discriminant functions for solving c‐class (c>2) pattern recognition problems, only one single feature, obtained by a single GP run, appears to be highly satisfactory in this approach. The proposed method is experimentally compared with some non‐linear feature extraction methods, such as kernel generalized discriminant analysis and kernel principal component analysis. Results demonstrate the capability of the proposed approach to transform information from the high‐dimensional feature space into a single‐dimensional space by automatically discovering the relationships between data, producing improved performance.     

A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability

In recent years, metric learning in the semisupervised setting has aroused a lot of research interest. One type of semisupervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale well with the data set size. In this letter, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.     

Information-theoretic Approaches Based on Sequential Monte Carlo to Collaborative Distributed Sensors for Mobile Robot Localization

We consider the Sequential Monte Carlo (SMC) method for Bayesian inference applied to the problem of information-theoretic distributed sensor collaboration in complex environments. The robot kinematics and sensor observation under consideration are described by nonlinear models. The exact solution to this problem is prohibitively complex due to the nonlinear nature of the system. The SMC method is, therefore, employed to track the probabilistic kinematics of the robot and to make the corresponding Bayesian estimates and predictions. To meet the specific requirements inherent in distributed sensors, such as low-communication consumption and collaborative information processing, we propose a novel SMC solution that makes use of the particle filter technique for data fusion, and the density tree representation of the a posterior distribution for information exchange between sensor nodes. Meanwhile, an efficient numerical method is proposed for approximating the information utility in sensor selection. A further experiment, obtained with a real robot in an indoor environment, illustrates that under the SMC framework, the optimal sensor selection and collaboration can be implemented naturally, and significant improvement in localization accuracy is achieved when compared to conventional methods using all sensors.     

Web挖掘在电子商务货源搜索中的应用

在电子商务环境下,货源信息的质量将直接影响企业业务经营水平。该文对电子商务环境下的货源信息搜索问题进行分析,提出以万维网为信息源,采用Web挖掘和信息检索技术设计实现货源搜索机器人的方法。该机器人内置元搜索引擎,可以扩大搜索范围,通过对搜索信息进行分析处理,从中发现有价值的货源相关信息。     

电磁环境三维可视化技术

针对当前比较复杂的电磁环境,在分析其应用需求的基础上,确定电磁环境可视化的几个基本要素,提出用三维可视化技术来构建电磁环境,采用直接体绘制、面绘制和切片绘制的方法对共进行可视化。利用这些方法实现了某区域电磁态势分析结果的可视化,试验结果证明该方法具有较好的可视化效果。     

生存迁移分析及其应用

通过模拟自然界生物的生存迁移过程建立一种自适应概率算法——生存迁移算法(LMA)。它模拟了生物通过比较生存状况和生存极限确定是否需要迁移,进而寻求更好生存状况的机制。生存状况使生物逐步了解未知的生存空间,生存极限使生物具有判断的标准,迁移使生物具有寻求生存的能力。给出LMA的机理和模型,分析LMA求解全局优化问题的收敛性。实验结果表明LMA在应用中具有较好的收敛性。     

基于主观逻辑的信任搜索算法

针对现有信任模型中信任搜索算法不能准确有效地对信任进行搜索与合成,利用主观逻辑理论对信任进行量化与计算,提出基于主观逻辑的信任搜索算法.算法由两个关键技术组成:信任路径提取算法与信任路径合成算法.仿真实例验证了该算法在信任合成时减少了信任网络中冗余信息对信任合成的影响,提高了信任搜索效率,使得合成结果更加准确.     

一种改进的主动拥塞控制算法

在已有的主动网络拥塞控制机制(ACC)中,通过丢包的方式缓减拥塞。TCP拥塞控制机制起主导作用,但不能满足现有网络环境要求。该文提出一种改进的ACC算法,该算法将拥塞控制参数嵌入到每个主动包中,通过驻留在主动路由器中的拥塞控制代码,对参数进行相应修改,从而达到拥塞预防和控制的目的。仿真结果表明,该方法具有较好的网络带宽利用率和公平性,丢包概率接近0。     

WSN中基于层次结构的可靠传输算法

提出针对无线传感器网络分层结构的一种数据可靠传输算法。采用综合节点剩余能量、缓冲区可用率、信道错误率3因素的可靠路径与备选可靠路径策略,依据缓冲区空闲情况进行预拥塞节点检测,进行路径前后疏导缓解拥塞。对簇头节点和非簇头节点的失效进行分类处理。仿真结果证明,该算法可提高数据传输的可靠性。     

无线传感器网络不等规模节能分簇路由算法

在分簇的无线传感器网络中,当簇头以多跳通信方式将数据传输至sink点时,越接近sink点的簇头过路数据负担越重,可能过早耗尽能量而导致传输失效,造成网络分割。该文提出一种不等规模节能分簇路由算法,通过限制成簇范围使接近sink节点的区域产生更多更小规模的簇。在分簇时形成源于sink节点的簇间跳数场,使数据经过最少的中间簇到达sink节点,并通过动态调整对下一跳簇的选择来平衡簇间负载。仿真结果表明,该算法延长了网络生命周期,有效降低了网络整体耗能。