An efficiency curve for evaluating imbalanced classifiers considering intrinsic data characteristics: Experimental analysis

Balancing the accuracy rates of the majority and minority classes is challenging in imbalanced classification. Furthermore, data characteristics have a significant impact on the performance of imbalanced classifiers, which are generally neglected by existing evaluation methods. The objective of this study is to introduce a new criterion to comprehensively evaluate imbalanced classifiers. Specifically, we introduce an efficiency curve that is established using data envelopment analysis without explicit inputs (DEA-WEI), to determine the trade-off between the benefits of improved minority class accuracy and the cost of reduced majority class accuracy. In sequence, we analyze the impact of the imbalanced ratio and typical imbalanced data characteristics on the efficiency of the classifiers. Empirical analyses using 68 imbalanced data reveal that traditional classifiers such as C4.5 and the k-nearest neighbor are more effective on disjunct data, whereas ensemble and undersampling techniques are more effective for overlapping and noisy data. The efficiency of cost-sensitive classifiers decreases dramatically when the imbalanced ratio increases. Finally, we investigate the reasons for the different efficiencies of classifiers on imbalanced data and recommend steps to select appropriate classifiers for imbalanced data based on data characteristics.National Natural Science Foundation of China (NSFC) 71874023 71725001 71771037 7197104

How electronic word of mouth dynamically influences product sales and supplies: an evidence from China film industry

As an important part of e-commerce, online reviews play a significant role in consumers’ purchase decisions. This study investigated the dynamic effects of electronic word of mouth (eWOM) and the number of people wishing to watch a movie on movie sales and supplies in the Chinese movie market. Using a dynamic simultaneous equation system and data of 76 films, this study analyzed the interrelationships between eWOM and movie sales and supplies. Our findings showed that both the volume and valence of eWOM affected movie sales and supplies significantly. The number of people who wanted to watch a movie had an opposite effect on movie sales and supply; eWOM volume had a positive impact on movie sales and supplies; and eWOM valence had a negative impact on movie sales and a positive impact on movie supplies. The number of people who wish to watch a movie was another important variable for movie sales and supplies, and it had a negative impact on the daily movie sales but a positive impact on the daily movie supplies. This study provided a detailed explanation of these results and thus contributed to improving the efficiency of movie suppliers’ utilization of online reviews

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers

Machine learning methods for systemic risk analysis in financial sectors.

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.This research has been partially supported by grants from the National Natural Science Foundation of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)

Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: a case from China

Trade-based Money Laundering, a new form of money laundering using international trade as a signboard, always appears along with speculative capital movement which has been accepted as the most concerned and consensus incentive giving rise to the collapse of the financial market. Unfortunately, preventing money laundering is very difficult since money laundering always has a plausible trade characterization. To reach this goal, supervision for regulator and financial institutions aims to effectively monitor micro entities’ behavior in financial markets. The main purpose of this paper is to establish a monitoring method including accurate recognition and classified supervision for Trade-based Money Laundering by means of knowledge-driven multi-class classification algorithms associated with macro and micro prudential regulation, such that the model can forecast the predicted class from the concerned management areas. Based on empirical data from China, we demonstrate the application and explain how the monitor method can help to improve management efficiency in the financial market. First published online 8 May 201

Machine learning methods for systemic risk analysis in financial sectors

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work. First published online 6 May 201

Social support and sleep quality in people with schizophrenia living in the community: the mediating roles of anxiety and depression symptoms

IntroductionResearch has demonstrated that higher social support is associated with better psychological health, quality of life, cognition, activities of daily living, and social participation, but the relationship between social support and sleep quality remains unknown. This study aims to investigate the mediating effects of anxiety and depression in the relationship between social support and sleep among community-dwelling patients with schizophrenia.MethodPurposive sampling was used to collect face-to-face data from 1,107 community-dwelling patients with schizophrenia in Chengdu, Sichuan Province, China, between April and July 2023. The Athens Insomnia Scale (AIS) was used to assess sleep quality; the Generalized Anxiety Disorder 7-item scale (GAD-7) was utilized to evaluate anxiety symptoms; and the Patient Health Questionnaire-9 (PHQ-9) was employed to assess depressive symptoms. The mediating effect of anxiety and depression symptoms was assessed using the bootstrap method via Model 6 (Serial multiple mediator model) of the SPSS PROCESS macro.ResultsAmong the 1,107 participants, the proportions of people with schizophrenia experiencing anxiety, depressive symptoms, and poor sleep quality were 22.8, 37.7, and 42.1%, respectively. Mediation analyses indicated that although social support had no direct effect on sleep quality, anxiety and depressive symptoms fully mediated the relationship between social support and sleep quality.ConclusionPatients with schizophrenia experience low levels of social support and poor sleep quality. To enhance the sleep quality of individuals with schizophrenia, all levels of society (government, medical institutions, and communities) must pay more attention to mental health. Implementing diverse intervention measures to strengthen social support and improve symptoms of anxiety and depression should be considered. This approach may potentially lead to an improvement in sleep quality among individuals with schizophrenia

Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.</p

An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement

The authors thank the editor and the anonymous referees for their valuable comments and insightful recommendations. This work was supported in part by grants from the National Natural Science Foundation of China (#71874023, #71771037, #71971042, #71910107002, and #71725001) and supported by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033.Urban resettlement projects involve a large number of stakeholders and impose tremendous cost. Developing resettlement plans and reaching an agreement amongst stakeholders about resettlement plans at a reasonable cost are some of the key issues in urban resettlement. From this perspective, urban resettlement is a typical large-scale group decision-making (GDM) problem, which is challenging because of the scale of participants and the requirement of high consensus levels. Observing that residents who are affected by a resettlement project often have tight social connections, this study proposes a framework to improve the consensus reaching and uses the minimum consensus cost to reduce the total cost for urban resettlement projects with more than 1000 participants. Firstly, we construct a network topology that consists of two layers to deal with incomplete social relationships amongst large-scale participants. An inner layer consists of participants whose preference similarities and trust relations are known. Meanwhile, an outside layer includes participants whose trust relations cannot be determined. Secondly, we develop a classification method to classify participants into small subgroups based on their preference similarities. We can then connect the participants whose trust relations are unknown (the outside layer) with the ones in the inner layer using the classification results. To facilitate effective consensus reaching in large-scale social network GDM, we develop a threestep approach to reconcile conflicting preferences and accelerate the consensus process at the minimum cost. A real-life urban resettlement example is used to validate the proposed approach. Results show that the proposed approach can reduce the total consensus cost compared with the other two practices used in the actual urban resettlement operations.National Natural Science Foundation of China (NSFC) 71874023 71771037 71971042 71910107002 71725001Spanish State Research Agency PID2019-103880RB-I00/AEI/10.13039/50110001103