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In the medical field, researchers are often unable to obtain the sufficient samples in a short period of time necessary to build a stable data-driven forecasting model used to classify a new disease. To address the problem of small data learning, many studies have demonstrated that generating virtual samples intended to augment the amount of training data is an effective approach, as it helps to improve forecasting models with small datasets. One of the most popular methods used in these studies is the mega-trend-diffusion (MTD) technique, which is widely used in various fields. The effectiveness of the MTD technique depends on the degree of data diffusion. However, data diffusion is seriously affected by extreme values. In addition, the MTD method only considers data fitted using a unimodal triangular membership function. However, in fact, data may come from multiple distributions in the real world. Therefore, considering the fact that data comes from multi-distributions, in this paper, a distance-based mega-trend-diffusion (DB-MTD) technique is proposed to appropriately estimate the degree of data diffusion with less impacts from extreme values. In the proposed method, it is assumed that the data is fitted by the triangular and trapezoidal membership functions to generate virtual samples. In addition, a possibility evaluation mechanism is proposed to measure the applicability of the virtual samples. In our experiment, two bladder cancer datasets are used to verify the effectiveness of the proposed DB-MTD method. The experimental results demonstrated that the proposed method outperforms other VSG techniques in classification and regression items for small bladder cancer datasets.

The cost of investing in new product development (NPD) is high, and it is a feasible way to use demand forecasts for customer or end-users as a decisive reference. However, this short-term time-series data has difficulties in learning because there is no past performance on which to base the estimates. In the past, it has been proven that the cumulative method of the fractional grey prediction model (FGM) is better than the traditional integer cumulative method of the grey model (GM) model. There are many studies using different optimal algorithms to determine the moderate score order. How to set the coefficient of α in FGM is also worth exploring. Therefore, this research reveals a new fractional grey prediction model which uses box-and-whisker plots to estimate the trends of data, known as the boxplot-based fractional scale prediction model (boxplot-based FGM, BP-FGM) to improve the accuracy of predictors by setting the coefficient sets of α. In the experiment, the examined dataset was collected from a well-known equipment manufacturer as the research object. For modeling, the mean absolute percentage error (MAPE) was established as the objective function of the optimization model, the results from three datasets verified the effect through the commodity attributes and public test data of its production, and the experimental results show that BP-FGM has better prediction results than FGM.

The Chinese language is the mother tongue that most students in Singapore need to master. However, for many local students, due to the use of English as the main language in Singapore’s families and the living environment, the time and frequency of using Chinese and the exposure to Chinese characters are relatively insufficient, which leads to a high forgetting rate, confusion of the characters and the improper use of Chinese characters. This study attempts to develop an app of a Chinese character learning game for Singapore primary school students, aiming to stimulate students’ interest in learning Chinese, increase their frequency of contact and use of Chinese characters, and ultimately strengthen their ability to remember, understand and use Chinese characters. By collecting the data from students’ questionnaire surveys, teachers’ questionnaire surveys, students’ literacy tests, and classroom observations, the research team found that the designed app can enhance the interest of lower grade primary school students in learning Chinese and strengthen their ability to memorize and use Chinese characters.

The problem of imbalanced data has a heavy impact on the performance of learning models. In the case of an imbalanced text dataset, minority class data are often classified to the majority class, resulting in a loss of minority information and low accuracy. Thus, it is a serious challenge to determine how to tackle the high imbalance ratio distribution of datasets. Here, we propose a novel classification method for learning tasks with imbalanced test data. It aims to construct a method for data preprocessing that researchers can apply to their learning tasks with imbalanced text data and save the efforts to search for more dedicated learning tools. In our proposed method, there are two core stages. In stage one, balanced datasets are generated using an asymmetric cost-sensitive support vector machine; in stage two, the balanced dataset is classified using the symmetric cost-sensitive support vector machine. In addition, the learning parameters in both stages are adjusted with a genetic algorithm to create an optimal model. A Yelp review dataset was used to validate the effectiveness of the proposed method. The experimental results showed that the proposed method led to a better performance subject to the targeted dataset, with at least 75% accuracy, and revealed that this new method significantly improved the learning approach.

Generative adversarial networks are known as being capable of outputting data that can imitate the input well. This characteristic has led the previous research to propose the WGAN_MTD model, which joins the common version of Generative Adversarial Networks and Mega-Trend-Diffusion methods. To prevent the data-driven model from becoming susceptible to small datasets with insufficient information, we introduced a robust Bayesian inference to the process of virtual sample generation based on the previous version and proposed its refined version, WGAN_MTD2. The new version allows users to append subjective information to the contaminated estimation of the unknown population, at a certain level. It helps Mega-Trend-Diffusion methods take into account not only the information from original small datasets but also the user’s subjective information when generating virtual samples. The flexible model will not be subject to the information from the present datasets. To verify the performance and confirm whether a robust Bayesian inference benefits the effective generation of virtual samples, we applied the proposed model to the learning task with three open data and conducted corresponding experiments for the significance tests. As the experimental study revealed, the integrated framework based on GAN and RBI, WGAN_MTD2, can perform better and lead to higher learning accuracies than the previous one. The results also confirm that a robust Bayesian inference can improve the information capturing from insufficient datasets.

Oversampling is the most popular data preprocessing technique. It makes traditional classifiers available for learning from imbalanced data. Through an overall review of oversampling techniques (oversamplers), we find that some of them can be regarded as danger-information-based oversamplers (DIBOs) that create samples near danger areas to make it possible for these positive examples to be correctly classified, and others are safe-information-based oversamplers (SIBOs) that create samples near safe areas to increase the correct rate of predicted positive values. However, DIBOs cause misclassification of too many negative examples in the overlapped areas, and SIBOs cause incorrect classification of too many borderline positive examples. Based on their advantages and disadvantages, a boundary-information-based oversampler (BIBO) is proposed. First, a concept of boundary information that considers safe information and dangerous information at the same time is proposed that makes created samples near decision boundaries. The experimental results show that DIBOs and BIBO perform better than SIBOs on the basic metrics of recall and negative class precision; SIBOs and BIBO perform better than DIBOs on the basic metrics for specificity and positive class precision, and BIBO is better than both of DIBOs and SIBOs in terms of integrated metrics.

In recent years, generative adversarial networks (GANs) have been proposed to generate simulated images, and some works of literature have applied GAN to the analysis of numerical data in many fields, such as the prediction of building energy consumption and the prediction and identification of liver cancer stages. However, these studies are based on sufficient data volume. In the current era of globalization, the demand for rapid decision-making is increasing, but the data available in a short period of time is scarce. As a result, machine learning may not provide precise results. Obtaining more information from a small number of samples has become an important issue. Therefore, this study aimed to modify the generative adversarial network structure for learning with small numerical datasets, starting with the Wasserstein GAN (WGAN) as the GAN architecture, and using mega-trend-diffusion (MTD) to limit the bound of virtual samples that the GAN generates. The model verification of our proposed structure was conducted with two datasets in the UC Irvine Machine Learning Repository, and the performance was evaluated using three criteria: accuracy, standard deviation, and p-value. The experiment result shows that, using this improved GAN architecture (WGAN_MTD), small sample data can also be used to generate virtual samples that are similar to real samples through GAN.

In the field of small-sample domains, since its introduction, the effectiveness and practicality of the megatrend diffusion (MTD) method have been demonstrated in various studies. Recently, with the popularity of generative deep learning, researchers have integrated Wasserstein generative adversarial networks (WGANs) with the MTD method and proposed a novel framework called WGAN-MTD for virtual data generation. It uses the MTD for producing estimates, which restricts the generative model's output value range and generates effective synthetic samples. However, the validity of developing virtual samples using real-world data containing outliers remains controversial, and the weight clipping method in WGAN has been shown to affect the stability of model training. In this study, we propose an advanced framework in which the boxplot is integrated with a penalization term to limit the effect of outliers, especially from small samples. The proposed framework considers the convolutional layers to capture local information features and lower the complexity of the model by reducing the number of parameters between the input and output layers. Additionally, we adopt a WGAN with the gradient penalty (GP) method instead of WGAN alone to improve the training stability and precision in the generative model. Experimental results demonstrate that both the boxplot and the penalization term enhance the accuracy of the generative models for small datasets containing outliers. [ABSTRACT FROM AUTHOR]

Misinformation on social media platforms has become a pervasive problem in recent years, with the potential to have a significant negative impact on society. One of the most effective countermeasures against misinformation is social correction, which refers to attempts to correct the source of misinformation. Social media platforms' misinformation reporting function (MRF) can be regarded as a form of social correction. However, current research on MRFs is limited, and there is a need to understand better the factors that affect users' intentions to use them. In this study, we aim to address this gap by integrating the expectation confirmation model (ECM) and protection motivation theory (PMT) to develop a research model that explains users' intentions to use MRFs. ECM posits that users' confirmation or disconfirmation of expectations determines their satisfaction with a new product or feature. PMT, on the other hand, emphasizes the role of threat appraisal and response appraisal in motivating behavior. The proposed research model hypothesizes that users' intentions to use MRFs can be affected by their threat appraisal of misinformation (including perceived severity and susceptibility), their response appraisal of MRFs (including self-efficacy, response efficacy, and response costs), and their confirmation or disconfirmation of expectations about MRFs. We conducted a quantitative study using a questionnaire survey to test the proposed research model. The questionnaire measured users' threat appraisal, response appraisal, confirmation or disconfirmation of expectations, and intentions to use MRFs. The data collected from the survey were analyzed using structural equation modeling. The findings of this study will have important implications for both theory and practice. Theoretically, the study will contribute to a better understanding of the factors affecting users' intentions to use MRFs. From a practical perspective, the study will provide valuable insights for social media platforms on designing and promoting MRFs that are effective in reducing the spread of misinformation. We explore the use of social media misinformation reporting capabilities. If the data collected from the MRF can be treated as training data for machine learning models and sensor-based misinformation detection systems, it will be possible to deal more effectively with the phenomenon of misinformation in social media through natural language processing and image analysis. [ABSTRACT FROM AUTHOR]

The TFT-LCD (thin-film transistor liquid crystal display) industry in Taiwan has been developed for decades. To provide better service for customers, most Taiwan companies in the supply chain have set up their warehouse in mainland China for vendor managed inventory (VMI). However, over the last decade, owing to the industry issues of product diversity strategies and short product lifecycles, it has become difficult for suppliers to control well the stock level in VMI, especially the operation of defective product returns. In order to keep high customer satisfaction, it is considered an option by forecasting possible future defectives for preparing the returning products to satisfy customers. The non-equigap grey model (NGM) used to be applied to such short-term time series data and has shown to be an effective tool. However, NGM predictions still can be improved by making more appropriate background values by determining their parameter a values. Accordingly, this paper employs the mega-trend-diffusion (MTD) technique to estimate better a values, and the model is thus called MTD-NGM(1,1). In the experiments, two data sets taken from a supplier are examined for effectiveness validation. The experimental results indicate that MTD-NGM(1,1) can generally produce better predictions than NGM(1,1). [ABSTRACT FROM AUTHOR]

Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive. Artificial Neural Networks are widely utilized to extract management knowledge from acquired data for its non-linear properties; however, getting as large a number of training data as needed is the fundamental assumption. This is often not achievable for pilot runs because there are few data obtained during trial stages and theoretically this means that the knowledge obtained is fragile. The purpose of this research is to utilize virtual sample generation techniques and the corresponding data effects to stabilize the prediction model. This research derives from using extreme value theory to estimate the domain range of a small data set, which is used for virtual sample production to fill the information gaps of sparse data. Further, for the virtual samples, a fuzzy-based data effect calculation system is developed to determine the comprehensive importance of each datum. The results of this research indicate that the prediction error rate can be significantly decreased by applying the proposed method to a very small data set. [ABSTRACT FROM AUTHOR]