RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification     

《Information processing & management》2023,60(2):103235

Imbalanced sample distribution is usually the main reason for the performance degradation of machine learning algorithms. Based on this, this study proposes a hybrid framework (RGAN-EL) combining generative adversarial networks and ensemble learning method to improve the classification performance of imbalanced data. Firstly, we propose a training sample selection strategy based on roulette wheel selection method to make GAN pay more attention to the class overlapping area when fitting the sample distribution. Secondly, we design two kinds of generator training loss, and propose a noise sample filtering method to improve the quality of generated samples. Then, minority class samples are oversampled using the improved RGAN to obtain a balanced training sample set. Finally, combined with the ensemble learning strategy, the final training and prediction are carried out. We conducted experiments on 41 real imbalanced data sets using two evaluation indexes: F1-score and AUC. Specifically, we compare RGAN-EL with six typical ensemble learning; RGAN is compared with three typical GAN models. The experimental results show that RGAN-EL is significantly better than the other six ensemble learning methods, and RGAN is greatly improved compared with three classical GAN models.  相似文献   

Creating value from Social Big Data: Implications for Smart Tourism Destinations     

Pasquale Del Vecchio  Gioconda Mele  Valentina Ndou  Giustina Secundo 《Information processing & management》2018,54(5):847-860

This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism.  相似文献   

Exploring the Fluctuations of Foreign Language Enjoyment in Conversation: An Idiodynamic Perspective     

Majid Elahi Shirvan  Nahid Talebzadeh 《Journal of Intercultural Communication Research》2018,47(1):21-37

The main focus of this study was to discern the effect of different topics on the dynamics of one of the main variables of positive psychology, foreign language enjoyment (FLE). To do this, we used an idiodynamic method to explore the rapid moment-to-moment changes of FLE. Seven female university students engaged in a conversation involving both simple and difficult topics, and self-rated their moments of experiencing enjoyment during the conversation process. The results indicate that the dynamicity of enjoyment emerges not only within individuals, but also across individuals intra-personally under the influence of conversational topics. Furthermore, the idiodynamic method provided us with the opportunity to examine the underlying factors of the dynamic fluctuations of FLE and gain a deeper insight into FLE as a dynamic system.  相似文献   

数据论文联合出版模式及数据论文出版     

马瀚青  杨小梅  侯春梅  李晓燕  李红星 《中国科技期刊研究》2018,29(7):698-703

【目的】 分析期刊编辑部与数据中心联合出版数据论文的模式，为进一步推进数据论文出版提供参考。【方法】 借鉴国际数据期刊论文出版模式，建立了期刊编辑部与数据中心联合出版数据论文的模式和流程，并分析了数据论文出版实践工作取得的成效和发展方向。【结果】 以期刊编辑部为主体，通过与数据中心联合，探索数据论文出版的流程，实现了数据论文的出版。【结论】 数据论文出版是一种新型论文形式，是对科学数据的肯定，能有效提高期刊的影响力。  相似文献   

Remaining in Control with an Illusion of Interactivity: The Paternalistic Side of Data Journalism*     

Ester Appelgren 《Journalism Practice》2019,13(8):956-960

  相似文献   

基于高纬稀疏数据分析的制造型企业研发投入对企业绩效的影响分析     

王勇  金涛 《科技管理研究》2018,(21)

国内外学者对科研投入与企业绩效之间的关系进行了大量研究和实证工作，现有研究多以定性研究为主，分析研发投入与企业绩效之间的关系以及影响这两者关系的直接和间接变量，有少量学者用定量分析的方法表达某个或者某几个变量与这两者关系之间的数学关系，但由于企业绩效及科研成果结果显现的影响变量太多，这种定量分析方法的局限性大。为探索一种对大量因素同时进行定量分析的方法，尝试采用对高纬稀疏数据进行分析，对沪深两市327家制造企业2011~2016年的数据进行分析，以“研发投入增长率>0 & 营业收入增长率>0 & 人均营业收入增长率>0”为分析目标，对影响目标实现的十类74个变量进行定量分析，并得到达到这一分析目标的定量优化规则：当期和上期单位研发人员研发投入、研发人员投入增长率、盈利能力、产品市场竞争度这五个变量处于一定范围内时，分析目标达成概率有较大幅度提升。这一分析结果既可以用于衡量企业研发水平是否达到规模边界，又可作为对企业研发投入行为对企业绩效结果的预判。  相似文献   

MARC to BIBFRAME: Converting the PCC to Linked Data     

Jacquie Samples  Ian Bigelow 《Cataloging & classification quarterly》2020,58(3-4):403-417

Abstract

The Program for Cooperative Cataloging (PCC) has formal relationships with the Library of Congress (LC), Share-VDE, and Linked Data for Production Phase 2 (LD4P2) for work on Bibliographic Framework (BIBFRAME), and PCC institutions have been very active in the exploration of MARC to BIBFRAME conversion processes. This article will review the involvement of PCC in the development of BIBFRAME and examine the work of LC, Share-VDE, and LD4P2 on MARC to BIBFRAME conversion. It will conclude with a discussion of areas for further exploration by the PCC leading up to the creation of PCC conversion specifications and PCC BIBFRAME data.  相似文献   

Using an interactive platform to recognize the intersection of social and spatial inequalities     

Lisa Poling  Travis Weiland 《Teaching Statistics》2020,42(3):108-116

With the creation of interactive tasks that allow students to explore spatial ways of knowing in conjunction with their other ways of knowing the world, we create a space where students can make sense of information as they organize these new ideas into their already existing schema. Through the use of a Common Online Data Analysis Platform (CODAP) and data from Public Use Microdata Areas (PUMA), students can explore the communities in which they live and work, critically examining opportunities and challenges within a defined space.  相似文献   

A reversible and secure patient information hiding system for IoT driven e-health     

《International Journal of Information Management》2019

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.  相似文献   

Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry     

《International Journal of Information Management》2019

Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization of industrial equipment, which generates extensive amounts of data. This information can be processed into useful knowledge with the use of machine learning algorithms. However, before the algorithms can effectively be applied, the data must go through an exploratory phase: assessing the meaning of the features and to which degree they are redundant. In this paper, we present the findings of the analysis conducted on a real-world dataset from a metallurgic company. A number of data analysis and feature selection methods are employed, uncovering several relationships, which are systematized in a rule-based model, and reducing the feature space from an initial 47-feature dataset to a 32-feature dataset.  相似文献