Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes

Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.     

Service improvement by business process management using customer complaints in financial service industry

In financial service industry, service improvement should be considered from process viewpoint and customer viewpoint because the value creation is ultimately linked with internal business processes on the back office and customers are involved as a co-producer of value. In this perspective, customer complaints through call centers are adequate to support the analysis for service improvement in financial service industry. In this study, we propose a web-based decision support system for business process management employing customer complaints, namely Voice of the Customer (VOC), and its handling data for service improvement. It involves VOC conversion for data enrichment and includes analysis of summarization, exception and comparison. The proposed system is evaluated on a major credit card company in South Korea.     

无线传感器网络定位理论和算法

定位技术作为网络协议和应用的基础,已经成为无线传感器网络重要的支撑技术,是传感器网络研究的核心问题之一.系统地总结了近年来定位理论和算法的最新研究进展.全面阐述了定位问题的形式化定义、定位问题复杂度分析、基于刚性理论的定位理论和定位问题可计算性研究的最新成果.通过对定位理论的研究可以更好地揭示定位技术的本质,回答很多定位技术相关的基本问题.此外,还深入分析了近年来典型的定位算法,介绍每种算法的设计思想,分析其适用范围和不足.最后给出定位理论和定位算法未来的研究方向.     

降低协同设计虚拟机启动开销的译码后指令缓存技术

协同设计虚拟机采用动态二进制翻译实现不同体系结构间的二进制兼容,对源指令的翻译和处理影响了协同设计虚拟机的启动性能.研究发现,在一个采用解释执行和翻译相结合的协同设计虚拟机中,处理非热点代码的解释执行是虚拟机启动开销的主要来源.发现了协同设计虚拟机中的解释例程局部性,并提出了一种硬件译码后指令缓存结构DICache(d...     

基于遥感的长沙市城市热岛效应时空分析

利用TM/ETM+数据,以长沙市为例,反演了地表温度,并针对不同时相的遥感数据,利用城市热岛强度来反映热岛效应强弱的变化。结果表明,长沙市的热岛空间分布与城市建成区的轮廓相吻合。城市热岛的范围随着城市建设、新建开发区以及道路交通网的发展不断增大,且在东南方向为主要增长方向。根据热岛强度鉴别并提取了强热岛区与正常区,发现长沙市强热岛区的面积已经从1993年的13.18km2扩大至2008年的68.16km2。最后,对长沙市热岛未来的发展趋势进行了预测。     

基于混合仿射模型配准的IBP超分辨率重建

针对传统的配准只考虑几何因素的不足,提出一种基于几何和光度混合仿射模型的配准迭代算法。该算法避免keren算法因为角度的泰勒级数展开所带来的误差,大幅提高配准精度,而且使图像亮度得到保持。与传统的仿射变换相比,平均平移误差精度达到了0.165个像素。采用迭代反投影算法对配准后的图像序列进行超分辨率重建。实验结果表明,超分辨率重建结果的信噪比和结构相似度有了较大提高。     

基于OpenGVS半潜船操作模拟器视景系统的研究

为了满足半潜船作业人员学习和培训的需要,有必要在航海模拟器大环境下研发半潜船操作模拟器仿真系统。以船舶静力学为基础,实现了半潜船装载重大件货物的过程的动态稳性计算,以Visual C++6.0和OpenGVS场景管理软件为开发平台,研制开发了半潜船操作模拟器应具备的视景系统。该仿真系统实现了视点的转移和跟踪,通过设置在半潜船的驾驶台、甲板及外部三个虚拟相机,实时动态显示半潜船各种运动的状态,同时实现了半潜船操作模拟器几个模块间的相互通信,取得了很好的效果。     

On fuzzy renewal processes for fuzzy random variables and extended theorems

In this paper, we construct fuzzy renewal processes involving fuzzy random variables. We first extend the renewal processes to the fuzzy renewal processes where interarrival times, rewards, and stopping times are all fuzzy random variables. According to these fuzzy renewal processes, we then extend some theorems of renewal processes to those in fuzzy renewal processes. These are elementary renewal theorem, asymptotic expected average reward, and Wald's equation. In each part, we also give examples for applications. © 2010 Wiley Periodicals, Inc.     

Relaxation method for unsteady convection–diffusion equations

We propose and implement a relaxation method for solving unsteady linear and nonlinear convection–diffusion equations with continuous or discontinuity-like initial conditions. The method transforms a convection–diffusion equation into a relaxation system, which contains a stiff source term. The resulting relaxation system is then solved by a third-order accurate implicit–explicit (IMEX) Runge–Kutta method in time and a fifth-order finite difference WENO scheme in space. Numerical results show that the method can be used to effectively solve convection–diffusion equations with both smooth structures and discontinuities.     

Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.