A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

TFRC friendliness and the case of ECN

The TFRC protocol has been proposed as a TCP‐friendly protocol to transport streaming media over the Internet. However, its deployment is still questionable because it has not been compared to other important protocols, analysed in the presence of important mechanisms, such as the explicit congestion notification (ECN), and studied under more realistic network conditions. In this paper, we address these three aspects, including other congestion control protocols not considered before in the same investigation, such as TCP Tahoe, Reno, Newreno, Vegas, Sack, GAIMD, and the Binomial algorithms, the effect of using ECN in the friendliness of the protocols, and the fairness of the protocols under static and dynamic network conditions. We found that TFRC can be safely deployed in the Internet if competing with TCP Tahoe, New Reno and SACK since fairness is achieved under all scenarios considered. We also found that ECN actually helps in achieving better fairness. However, fairness problems arise when TFRC competes with TCP Reno, GAIMD, SQRT or IIAD in static or dynamic conditions, or both. We used normalized throughput, fairness index, and convergence time as the main performance metrics for comparison. Copyright © 2004 John Wiley & Sons, Ltd.     

Applicant reactions to face-to-face and technology-mediated interviews: A field investigation.

This field study examined applicant reactions (N = 802) toward face-to-face as compared with technology-mediated interviews (through videoconferencing or by telephone) for 346 organizations. Face-to-face interviews were perceived as more fair and led to higher job acceptance intentions than were videoconferencing and telephone interviews. Perceived interview outcomes were higher with face-to-face and telephone interviews over videoconferencing. Self-monitoring moderated the relationship between interview medium and perceptions of fairness. Specifically, this relationship was (a) positive for face-to-face, (b) negative for telephone, and (c) nonsignificant for videoconferencing interviews. Moreover, the number of offers an applicant received moderated the relationship between interview medium over, and perceived fairness. The relationship between number of offers and perceived fairness was positive for face-to-face and negative for technology-mediated interviews. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

综合运用激励理论调动营销人员的积极性

从企业营销工作实践和激励理论相结合的角度,论述了企业如何设计营销的激励和约束机制,提高销售人员的工作效率和积极性.     

车身曲面的光顺品质分析与应用

文章对车身反求工程中曲面光顺的含义、常用光顺检查方法( 曲面曲率法、光照模型法、等高线法、线性变换法)的特点及原理进行论述;介绍CATIA软件Freestyle模块的外形分析功能的使用,其目的是在图形终端屏幕上直观分析曲面光顺品质,满足用户对产品外形的视觉要求,给汽车外观赋予浓郁的时代感.     

图书馆促进社会包容性发展的思考

促进社会包容性发展是图书馆从诞生伊始就坚守的价值观,通过保障信息自由与信息公平、促进多元文化理解、提高公民媒介信息素养等途径,图书馆致力于支持和促进社会包容性发展。图书馆需要进一步强化服务意识、加强跨组织协同、切实发挥信息素养培养及社区教育的服务职能、促进社区参与,为社会包容性发展提供更大支持。     

混合偏好模型下的分布式理性秘密共享方案

理性秘密共享方案通过扩展参与者的类型后具有更好的适应性,而现有方案中的共享秘密往往依赖于秘密分发者,但在某些特定环境中秘密分发者并不一定存在.通过对传统分布式秘密共享方案的分析,给出了分布式理性秘密共享方案的一般形式化描述;同时,考虑理性参与者的眼前利益和长远利益,提出一种新的理性参与者混合偏好模型;进一步结合机制设计理论的策略一致机制,设计了一个激励相容的信誉讨价还价机制,以此有效约束理性参与者的行为,从而实现了公平的(t,n)(t,n≥2)分布式理性秘密共享方案的构造;通过从信道类型、秘密分发者的在线/离线需求、方案的通用性和偏好模型等方面与目前相关理性秘密共享方案进行对比分析,进一步分析了所提出方案的优势.     

一种新型的WiMAX基站实时服务调度算法

为满足Wi MAX对Qo S的需求并克服MSIR调度算法存在的不足,本文提出了一种新型的基于截止期限最大信号干扰比的Wi MAX基站实时服务调度算法。这种算法根据链路质量和截止期限两个标准对数据包进行调度。首先从Wi MAX调度算法的Qo S架构出发,给出了信道模型;其次详细分析了本文提出方法的具体实现过程;最后,借助NS-2软件平台进行了建模与仿真。仿真结果表明,本文提出的方法减小了平均停留时间并有效提高了吞吐量和公平性指数,因此具有可行性和可靠性。     

一种基于正弦映射的混沌交织器设计及应用*

移动信道编码中交织器的设计直接影响Turbo码的距离谱和性能,其主要作用是提高低重序列的输出码重和减小译码输出之间的相关性。随机交织器在数据帧较长时性能比较好,通过码序列位置重置实现交织,利用正弦函数的混沌映射有很好的随机性和瞬时正交性的特点,实现了一种新型混沌随机交织器,提高了交织器的误码率性能,并且在伽利略卫星系统中应用达到接近理论值的编码效率和误码率的特性。     

一种新的紫外光自组织通信网络MAC层避退算法

紫外光自组织通信网络是把紫外光与自组织网络相结合的,能够利用自组织网络的多跳特性来克服紫外光距离有限性的通信网络。为了使紫外光自组织通信网络中各个节点能够对有限的信道资源进行充分与公平的利用,需要对网络协议栈中的第二层——媒介接入控制层上的传统算法进行优化。本文以媒质接入控制层(MAC,Media Access Control)传统上采用的二进制指数退避算法为基础,以提高公平性为目的,提出了依据退避计数器值的随机选取是否合理,继而采取相应的奖励惩罚机制的新算法。研究结果表明:新算法提高了无线紫外光自组织通信网络中各节点接入信道的公平性,并在信道吞吐量和信道接入公平性之间达到了一个较好的折衷。