Institution
Guangdong University of Foreign Studies
Education•Guangzhou, China•
About: Guangdong University of Foreign Studies is a education organization based out in Guangzhou, China. It is known for research contribution in the topics: China & Computer science. The organization has 2788 authors who have published 3003 publications receiving 24118 citations.
Papers published on a yearly basis
Papers
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01 Feb 2011TL;DR: The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
Abstract: This paper presents a review of - and classification scheme for - the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject has yet to be carried out. This paper thus represents the first systematic, identifiable and comprehensive academic literature review of the data mining techniques that have been applied to FFD. 49 journal articles on the subject published between 1997 and 2008 was analyzed and classified into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud, and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that data mining techniques have been applied most extensively to the detection of insurance fraud, although corporate fraud and credit card fraud have also attracted a great deal of attention in recent years. In contrast, we find a distinct lack of research on mortgage fraud, money laundering, and securities and commodities fraud. The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data. This paper also addresses the gaps between FFD and the needs of the industry to encourage additional research on neglected topics, and concludes with several suggestions for further FFD research.
917 citations
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TL;DR: Analyses of panel data of 332 new products from Amazon.com over nine months reveal that the valence of reviews and the volume of page views have a stronger effect on search products, whereas theVolume of reviews is more important for experience products.
Abstract: This study examines the effect of online reviews on new product sales for consumer electronics and video games. Analyses of panel data of 332 new products from Amazon.com over nine months reveal that the valence of reviews and the volume of page views have a stronger effect on search products, whereas the volume of reviews is more important for experience products. The results also show that the volume of reviews has a significant effect on new product sales in the early period and such effect decreases over time. Moreover, the percentage of negative reviews has a greater effect than that of positive reviews, confirming the negativity bias. Thus, marketers need to consider the distinctive influences of various aspects of online reviews when launching new products and devising e-marketing strategies.
500 citations
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TL;DR: Abundance of natural resources mitigates CO2 emission in Russia, but contributes to pollution in South Africa, and natural resources help to form Environmental Kuznets Curve (EKC) hypothesis in Brazil, China, Russia, and South Africa.
491 citations
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TL;DR: Based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method.
Abstract: Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts' assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being's subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method.
419 citations
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TL;DR: A systematic literature review of empirical studies on ML model published in the last two decades finds that eight types of ML techniques have been employed in SDEE models, and overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models.
Abstract: Context: Software development effort estimation (SDEE) is the process of predicting the effort required to develop a software system. In order to improve estimation accuracy, many researchers have proposed machine learning (ML) based SDEE models (ML models) since 1990s. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Objective: This research aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. Method: We performed a systematic literature review of empirical studies on ML model published in the last two decades (1991-2010). Results: We have identified 84 primary studies relevant to the objective of this research. After investigating these studies, we found that eight types of ML techniques have been employed in SDEE models. Overall speaking, the estimation accuracy of these ML models is close to the acceptable level and is better than that of non-ML models. Furthermore, different ML models have different strengths and weaknesses and thus favor different estimation contexts. Conclusion: ML models are promising in the field of SDEE. However, the application of ML models in industry is still limited, so that more effort and incentives are needed to facilitate the application of ML models. To this end, based on the findings of this review, we provide recommendations for researchers as well as guidelines for practitioners.
403 citations
Authors
Showing all 2838 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xin Sun | 63 | 729 | 16851 |
Yong Hu | 46 | 470 | 9098 |
Chao Deng | 44 | 213 | 5487 |
David Carless | 39 | 90 | 7086 |
Hai Xu | 38 | 237 | 5577 |
Wei Ren | 31 | 377 | 4243 |
Stephen Nicholas | 26 | 153 | 2561 |
Geng Cui | 26 | 72 | 2828 |
Sven Tarp | 23 | 116 | 1860 |
Mei Liu | 23 | 66 | 1990 |
Haitao Liu | 23 | 125 | 1839 |
Mingdong Tang | 22 | 70 | 1673 |
Tao Gong | 20 | 87 | 1176 |
Chao Ma | 20 | 68 | 1214 |
Tianyong Hao | 20 | 125 | 1136 |