Accurate ensemble pruning with PL-bagging

Ensemble pruning deals with the selection of base learners prior to combination in order to improve prediction accuracy and efficiency. In the ensemble literature, it has been pointed out that in order for an ensemble classifier to achieve higher prediction accuracy, it is critical for the ensemble classifier to consist of accurate classifiers which at the same time diverse as much as possible. In this paper, a novel ensemble pruning method, called PL-bagging, is proposed. In order to attain the balance between diversity and accuracy of base learners, PL-bagging employs positive Lasso to assign weights to base learners in the combination step. Simulation studies and theoretical investigation showed that PL-bagging filters out redundant base learners while it assigns higher weights to more accurate base learners. Such improved weighting scheme of PL-bagging further results in higher classification accuracy and the improvement becomes even more significant as the ensemble size increases. The performance of PL-bagging was compared with state-of-the-art ensemble pruning methods for aggregation of bootstrapped base learners using 22 real and 4 synthetic datasets. The results indicate that PL-bagging significantly outperforms state-of-the-art ensemble pruning methods such as Boosting-based pruning and Trimmed bagging.     

Effects of visual and linguistic anthropomorphic cues on social perception,self-awareness,and information disclosure in a health website

Acknowledging the lack of studies examining both visual and linguistic anthropomorphic cues and the underlying mechanisms of their effects, we investigated how the different modalities of anthropomorphic cues in a health website influenced information disclosure. In a 2 (visual cues: human vs. non-human image) × 2 (linguistic cues: conversational vs. impersonal language) × 2 (question type: less vs. more sensitive questions) between-subjects experiment (N = 254), participants registered with a mock-up health website. We assessed a behavioral outcome of not disclosing personal information and psychological outcomes of social perception and self-awareness as potential mediators. Results revealed distinctive effects of the two modalities of the anthropomorphic cues. Anthropomorphic images, on one hand, increased public and private self-awareness, and public self-awareness in turn led to less information disclosure. Anthropomorphic language, on the other hand, heightened social perception and promoted information disclosure, but social perception did not predict the disclosure. These results indicate unique underlying mechanisms of the effects of anthropomorphism: priming effect of visual cues, and communicative effects of linguistic cues.     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.     

论版面设计的视觉符号语言

阐述了视觉符号语言在版面设计中的编码与各种表现形式，强调信息社会的来临，以视觉为形态的符号语言将有效加速信息的传递。     

柔性物流技术在生产中的应用

东风汽车公司立足于自己特殊的地理环境及发展历程，经过30年的实践和探索，形成适合自身的物流组织，如：采用合理的计划实施多品种混流和同步化生产、对跨厂际零部件采用物流组织配送制等，运用MRP与JIT相结合的先进的自动化采集技术，建立管理信息系统，使企业的生产经营工作取得了同步的发展。     

Florida DOT Project-Level Bridge Management Models

The Florida Department of Transportation (FDOT) is in the process of implementing Pontis, a Bridge Management System, to provide decision support to engineers in the headquarters and district offices as they make routine policy, programming, and budgeting decisions regarding the preservation and improvement of the state’s bridges. As part of this effort, an ongoing research program is underway to adapt the system to FDOT needs as well as to advance the state of the art in several areas important to the Department. Most of the research results are organized around a new project-level decision support framework that complements and builds on Pontis’ existing network-level analysis. Specific new models include accident risk and user cost due to roadway width and alignment deficiencies; user cost of load capacity and vertical clearance restrictions, and moveable bridge openings; project-level prediction models for bridge element condition and costs; and prediction of economics of scale and scoping possibilities. The new models are built into a highly graphical spreadsheet model for decision support use.     

User interfaces for communication bridges across the digital divide

Connecting people across the “digital divide” is as much a social effort as a technological one. We are developing a community-centred approach to learn how interaction techniques can compensate for poor communication across the digital divide. We have incorporated the lessons learned regarding social intelligence design in an abstraction and in a device called the SoftBridge. The SoftBridge allows communication to flow from endpoints through adapters, getting converted if necessary, and out to destination endpoints. Field trials are underway with two communities in South Africa: disadvantaged Deaf users and an isolated rural community. Initial lessons learned show that we have to design user interfaces that allow users to understand and cope with delay. We also learned that social concerns are often more important than the technical issues in designing such systems.     

Web服务在信用信息工程中的应用

目前的社会信用信息工程中,许多的政府部门都建立了自己的信用信息数据库,但在政府各级部门数据库之间进行数据交流和共享存在着许多的问题,在许多地方,数据共享还是一片空白。Web服务技术是应用程序通过内联网或者因特网发布和利用软件服务的一种标准机制。利用WEB服务可以方便地实现不同系统之间的数据交换和集成。该文提出利用web服务技术来解决信用信息工程中的信息数据共享问题。并在此基础上提出利用WEB服务工作流技术将信用评估整合到电子商务过程,以实现可信用电子商务     

Stream Classification System Based on Susceptibility to Algal Growth in Response to Nutrients

We developed a stream classification system that is based on stream’s susceptibility to algal growth using a two-step approach. The model portrays algal biomass as a result of stream’s response to nutrient concentrations and the response is governed by various stream factors. In the first step, a nutrient-chlorophyll a relationship was developed to characterize nutrient’s effects on algal biomass. Residuals of the relationship were attributed to stream’s susceptibility to algal growth in response to nutrients and referred to as “observed” susceptibility. In the second step, conditions of other contributing factors were used to explain the variation in the residuals and the developed relationship was used to generate “predicted” susceptibility. Existing data compiled from various monitoring projects of Illinois streams and rivers were used to illustrate the approach. Streams were classified into three (high, medium, and low) categories based on their observed and predicted susceptibility values, respectively. With the available data, the model showed a 40-50% success rate for classifying the streams based on three observed and predicted susceptibility categories. Model entropy also was calculated for selecting the best model. The results show the important role of both nutrients and other contributing factors in explaining the variation of algal biomass. The study also suggests ways to fine tune the model and improve its accuracy, which would make the presented model a more viable tool for stream classification for establishing nutrient criteria to prevent surface streams from eutrophication.