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• Exploring the effect of locally weighted learning with machine learning model. • Gain ratio are used to select the most important environmental factor. • LWL-RS-ADT appears as an accurate model in assessing landslide susceptibility. Assessing landslide susceptibility and predicting the possibility of landslide event is the foundation and prerequisite for emergency response and management of landslide disaster. The target of current paper is to propose five integration models based on integrating locally weighted learning (LWL) with a radial basis function classifier (RBF), Fisher's linear discriminant (FLDA), quadratic discriminant analysis (QDA), a Credal decision tree (CDT), an alternating decision tree (ADT) and random subspace (RS) and the performance of five integration models were compared for modeling landslide susceptibility. Yongxin County from China was employed as a study area, 364 landslide locations and fifteen environmental factors were applied. The results demonstrate that the proposed LWL-RS-ADT model is more reliable and stable than the other models. Among the fifteen environmental factors, NDVI, lithology, and altitude are the very significant factors in the six models. It is concluded that the proposed integration models provide an effective way to predict the susceptibility of landslides. [ABSTRACT FROM AUTHOR]

Predicting the stock price is challenging because of its volatility, high dimensions, and complex non-linearity. The multitask learning methods can capture the internal relationship among sub-tasks and obtain better prediction effect than the traditional single-task learning methods. However, most multitask learning methods ignore the inherent distribution of the original samples, which fails at achieving good generalization performance. In this paper, we first present an asymmetric squared ɛ -insensitive loss function, which can improve the generalization ability of the regressor by adjusting the asymmetric parameter. Then, an asymmetric proximal support vector regression (a-PSVR) model is proposed, which greatly improves the flexibility of proximal support vector regression (PSVR). Based on different multitask learning assumptions, two multitask learning asymmetric proximal support vector regression algorithms, i.e., MTL-a-PSVR and EMTL-a-PSVR, are advanced. Both multitask learning algorithms can obtain optimal solutions by solving quadratic programming problems. Additionally, a special case of multitask learning proximal support vector regression (MTL-PSVR) is introduced by analyzing the asymmetric squared ɛ -insensitive loss function. To illustrate the merit of the methods, the proposed models are applied to predict the trends of stock market indices in China and the U.S. and stock prices of four Chinese securities companies. The experimental results demonstrate the significant advantages of the proposed algorithms in prediction effect and generalization performance. • An ɛ -insensitive loss function is presented for asymmetrically distributed data. • The a-PSVR has better prediction performance than proximal support vector regression. • A multitask joint learning model is proposed to perform the stock market forecast. • The cross-relations and asymmetry among stock data were considered simultaneously. [ABSTRACT FROM AUTHOR]

Advanced decision support for rail traffic control is significant for enhancing the safety and quality of railway transport service. Data-driven methods have shown powerful learning ability and wide extensibility for prediction, classification, and decision-making problems. In this paper, we propose a hybrid prediction model based on deep forest (DF) ensemble learning to analyze the two common rail traffic control actions, i.e., changing the dwelling times and running times. This basic concept is to mimic the decision-making of rail traffic controllers, providing them with advanced decisions/control actions using data-driven learning algorithms. According to the decision-making approach of rail traffic controllers, the learning process of the model is split into two stages, i.e., learning the type of action (ToA) and the number of changes (NoC) in the dwelling times and running times (i.e., how many dwelling times or running times have been changed compared with the planned ones). The first stage is a classification problem; thus, DF classifier with the synthetic minority oversampling technique (SMOTE) is employed to deal with imbalanced data. In the second stage, the DF regressor treats the NoC in the dwelling and running times as numerical variables and utilizes the information from stage one, i.e., the prediction results of the classification model, to make predictions. The proposed model is calibrated on train operation data from two high-speed railway lines in China. The experimental results and comparative analyses show that the proposed method provides advanced and timely decision support for controllers. These characteristics of the proposed model are imperative for supporting the dynamic decision-making of controllers to manage railway traffic. [ABSTRACT FROM AUTHOR]

• Propose an innovative Bayesian model to control the risk of COVID-19 patients. • Propose two indicators to verify the feasibility and effectiveness of the model. • Provide the tool for risk assessment in the field of epidemics. In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics. [ABSTRACT FROM AUTHOR]

Corporate financial distress prediction research has been ongoing for more than half a century, during which many models have emerged, among which ensemble learning algorithms are the most accurate. Most of the state-of-the-art methods of recent years are based on gradient boosted decision trees. However, most of them do not consider using feature importance for feature selection, and a few of them use the feature importance method with bias, which may not reflect the true importance of features. To solve this problem, a heuristic algorithm based on permutation importance (PIMP) is proposed to modify the biased feature importance measure in this paper. This method ranks and filters the features used by machine learning models, which not only improves accuracy but also makes the results more interpretable. Based on financial data from 4,167 listed companies in China between 2001 and 2019, the experiment shows that compared with using the random forest (RF) wrapper method alone, the bias in feature importance is indeed corrected by combining the PIMP method. After the redundant features are removed, the performance of most machine learning models is improved. The PIMP method is a promising addition to the existing financial distress prediction methods. Moreover, compared with traditional statistical learning models and other machine learning models, the proposed PIMP-XGBoost offers higher prediction accuracy and clearer interpretation, making it suitable for commercial use. • The model Combines a corrected feature selection measure and XGBoost. • Permutation importance can correct the bias of feature importance. • The model is validated on Chinese listed companies datasets over five metrics. • The model is proved to outperform several benchmark techniques. • The feature importance and partial dependence plot enhance model interpretation. [ABSTRACT FROM AUTHOR]

• European option datasets were time-sequenced by maturity to extract time information. • Two deep learning models were built for the option pricing tasks. • Our models can price option accurately without volatility calibration and simulation. • An interpretable method was used to find how models improved the pricing results. • Accumulate local effect range method was proposed to get feature emphasis of models. In this paper, we investigated the feasibility of pricing European options with time-sequencing data processing method and deep learning models, based on two European options, the ETF50 options of China and the S&P 500 options of America. Four competing models were built to verify the improvement of the 1D-CNN and LSTM models on the option pricing task. Methods like cross-validations and statistical tests were also used to make our experiments more robust. Besides, in order to increase the stability and the interpretability of our pricing models, we selected the ALE method to interpret and analyze the behavior of the deep learning models. The empirical results indicate that, in both ETF50 option and S&P500 option pricing tasks, the 1D-CNN and LSTM models had significant advantages in forecasting accuracy and robustness under moneyness, trading date or maturity dimension irrespectively. Especially for the LSTM model, which has robust performance using different kinds of cross-validation methods. With the help of ALE method, we proved that the improved performance brought by the 1D-CNN and LSTM models could be attributed to their capability of capturing time-series information and their different emphasis on input features and lags. [ABSTRACT FROM AUTHOR]