基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。     

大数据时代进行服务创新的思考

在大数据时代,服务创新必须与超级计算技术密切结合,同时要注意一些应该充分体现的原则。可以开展服务创新的领域是极其广泛的,在经济管理领域,主要可以在企业经营决策、金融理财、商业模式、选择业务外包等诸多领域提供创新的服务。     

Operating efficiency in Chinese universities: An extended two-stage network DEA approach

The result shows that it accepts the null hypothesis. Namely, there is no significant difference in the operating efficiency of universities in different regions. That is to say, although the efficiency of the central and western universities is slightly better than that of the eastern universities in terms of the average efficiency, there is no significant efficiency difference among the eastern, central, and western regions statistically. Therefore, it shows a balanced development trend for the efficiency of universities in different regions.     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.     

An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities

This paper shows efficiency indices for 60 Brazilian electricity distribution utilities. The efficiency scores are gauged by three DEA models. For both models, these quantities are evaluated under different contexts. One treats with respect to the regulator perspective. The others examine an alternative approach based on cluster analysis and restrictions on factor weights. It is worth pointing out that these developments can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental in incentive regulation schemes.     

Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Mobile payment and online to offline retail business models

Mobile payments are services that use mobile devices to make payments. When digitalization moves across channel boundaries, online to offline channel retail will expand. Online to offline retailing will become the future retail owner stream and retail operators will move from cross-channel or multi-channel to omni-channel. This study investigates a market survey in Taiwan developing a data mining analytics including clustering analysis and association rules based on a snowflake schema database design. The role of mobile payment is determined in terms of new retail payment mechanism that promotes a better consumer purchase experience in an online to offline business environment.     

Coloring the destination: The role of color psychology on Instagram

Color psychology plays important roles in product packaging, the retail environment, and online marketing. This study analyzed current practices related to color composition in tourism-related photographs on Instagram. Data mining and pictorial content analysis were adopted to investigate how lightness, chroma, and hue may influence Instagram posts' popularity. Generally, individuals appeared more likely to respond to brighter and more saturated destination pictures. Orange, yellow, blue, and violet contributed significantly to post popularity based on different photographic typologies. Overall, color is a complex construct that functions within diverse dimensions and can enrich tourists’ destination-related perceptions. Based on brand post popularity and color theories, the study findings provide valuable implications for effective and efficient destination promotion via Instagram. Relevant limitations and future directions are also discussed.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.