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Accurately predicting buffet frequency and aerodynamic noise level is crucial in transonic buffet noise reduction studies. In this study, the Random Forest (RF) algorithm is employed to predict the Power Spectral Density (PSD) and Overall Sound Pressure Level (OASPL) distribution over the supercritical airfoil RAE2822. The study indicates that the RF algorithm exhibits greater advantages over the Multi-Layer Perceptron (MLP). This algorithm does not suffer from the problems of high-frequency divergence or inflection distortion, maintaining a reduction in prediction errors for OASPL and PSD by approximately three to four orders of magnitude. Additionally, this paper proposes a priori criterion for evaluating the accuracy of PSD prediction. When there is a strong correlation between PSD at adjacent points, a data-driven modeling approach can achieve higher prediction accuracy. However, when the root mean square error of the cross-correlation functions, auto-correlation functions, and the PSD between adjacent points are both high, the generalization ability of pure data-driven modeling is insufficient, necessitating the additional monitoring points to ensure prediction accuracy. [ABSTRACT FROM AUTHOR]

One of the most important hydraulic structures that has been overlooked is culverts. Regular maintenance and inspections are required to ensure that these structures are used effectively and safely. Culvert inspection, on the other hand, takes a significant amount of time and resources, and agencies cannot afford to do it on a regular basis. To overcome physical inspection challenges, this study proposed employing machine learning algorithms to predict the condition of Utah's culverts based on historical data. This study assessed multiclass classification algorithms that had not been used before. The classification machine learning algorithms employed in this study are Random Forest, Decision Tree, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network. Also, a combined dataset from four states was used to address Utah's limited culvert inventory data. The final dataset contained 2555 culvert records. Out of the developed prediction models, Random Forest had the highest accuracy of 82%. The findings demonstrated that Random Forests could be used to improve culvert network quality while reducing culvert maintenance expenses for UDOT. The proposed model may be applied to similarly existing culvert inventories located around the nation. [ABSTRACT FROM AUTHOR]

Tree-based ensemble algorithms (TEAs) have had a transformative impact in various fields. However, when they are applied to real-time critical problems such as medical analysis, existing TEAs fall short of two-fold issues. The first is the trade-off among model interpretability, accuracy, and explainability. Second, traditional TEAs perform dramatically worse when the number of contributing features to model accuracy is small in comparison to the total number of features. We address the aforementioned issues in this paper and propose a novel "eXplainable Reasonably Randomised Forest" (XRRF) algorithm. The XRRF consists of four conclusive steps: learning performance, intrinsic interpretability, model accuracy, and explainability. The proposed forest algorithm is evaluated on three real-world problems (medical analysis, business analysis, and employee churn), a hybrid artificial dataset, and twenty multidisciplinary benchmark problems with varying characteristics. The experimental results demonstrate that the XRRF outperforms the six mainstream and two cutting-edge black-box and white-box algorithms, respectively. [ABSTRACT FROM AUTHOR]

The black‐tailed prairie dog (Cynomys ludovicianus) is a keystone species of North American short‐ and mixed‐grass temperate grasslands with critical importance for multiple species of conservation concern. Low population numbers due to habitat and social constraints as well as disease require conservation and management actions. To identify potential habitat and future relocation sites, we evaluated 6 statistical techniques to predict the optimal resource selection function model for black‐tailed prairie dog habitat in 3 Native nations of Montana. Random forest models performed best across our study area and included grass coverage and climatic and topographic variables as the most important predictors of prairie dog occupancy. Using predictive habitat models in conjunction with land‐use designations can refine conservation planning to support wildlife population objectives where there are foundations for long‐term success. Our analysis also serves as a guide for assessing habitat suitability, connectivity, and restoration for additional species dependent or interconnected with black‐tailed prairie dogs. [ABSTRACT FROM AUTHOR]

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression (OR) problem. Recent methods formulate an OR problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel OR approach, termed convolutional OR forest (CORF), for image ordinal estimation, which can integrate OR and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for OR by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e., facial age estimation and image esthetic assessment, showing significant improvements and better stability over the state-of-the-art OR methods. [ABSTRACT FROM AUTHOR]

The random forest classifier is widely used in different fields due to its accuracy and robustness. Since its invention, the random forest algorithm is naturally developed for multi-dimensional vectorial data since features can be directly sampled during the decision tree construction procedure. In the context of discrete sequence classification, an explicit feature set is not readily available and we need to employ a feature extraction algorithm before building the random forest. However, such a predefined feature subset may limit the diversity of decision trees since the set of candidate features is composed of all subsequences. As a result, the predictive accuracy of constructed random forest classifier may be reduced. To address this, we propose a new algorithm that is able to directly build a random forest by choosing features from the set of all subsequences adaptively. To improve the running efficiency of our algorithm, the count-suffix tree is utilized to facilitate the fast frequency counting of subsequences so as to accelerate the generation of each randomized decision tree. The experimental results on 15 real datasets show that our method can outperform those state-of-the-art classification algorithms in terms of the predictive accuracy. The source code of our method can be found at: https://github.com/JiaqiWang-dlut/RSForest. [ABSTRACT FROM AUTHOR]

• Highlight soccer detection is a hard but relevant task for match summarization. • An automatic system for highlight soccer identification is proposed • Highly discriminative low-level audio and video features are produced. • It selects a sparse subset of representative frames from long-term sliding windows. • It achieve s a match-time compression rate of 94%, identifying all highlights (100%). The massive growth of audiences eager for sport content has substantially increased workers' demand in this profitable segment. Highlight identification is vital for summarizing football matches. Decision support tools can significantly reduce the number of company employees required to tackle such a task, widely benefiting workforce resource allocation. This paper discusses the development of an automatic football highlight detector. The proposed system exploits discriminative low-level audio and video features extracted from a compact set of irregularly time–spaced frames that integrate a long-term sliding window. A new mixed wrapper-probabilistic algorithm leverages a cost-effective selection of the most significant frames submitted to a robust multi-frame consensus classification scheme. By considering a comprehensive database integrating 30 full matches, the proposed approach achieves a highlight identification rate of 100% (including all annotated goals), conjugated with a match-time compression rate of about 94%, when employing a Random Forest classifier. [ABSTRACT FROM AUTHOR]

The foremost requirement for a decision forest to achieve better ensemble accuracy is building a set of accurate and diverse individual decision trees as base classifiers. Existing decision forest algorithms mainly differ from each other on how they induce diversity among the decision trees. At the same time, most of the drawbacks of existing algorithms originate from their induction processes of diversity. In this paper, we propose a new decision forest algorithm that is more balanced through effective synchronization between different sources of diversity. The proposed algorithm is balanced theoretically and empirically. We carried out experiments on 25 well-known data sets that are publicly available from the UCI Machine Learning Repository, to perform an extensive empirical evaluation. The experimental results indicate that the proposed algorithm has the best average ensemble accuracy rank of 1.8 compared to its closest competitor at 3.5. Using the Friedman and Bonferroni-Dunn tests, we also show that such an improvement is indeed statistically significant. In addition, the proposed algorithm is found to be competitive in terms of complexity and other relevant parameters. [ABSTRACT FROM AUTHOR]

Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters. [ABSTRACT FROM AUTHOR]

Although interaction effects can be exploited to improve predictions and allow for valuable insights into covariate interplay, they are given limited attention in analysis. Interaction forests are a variant of random forests for categorical, continuous, and survival outcomes that explicitly models quantitative and qualitative interaction effects in bivariable splits performed by the trees constituting the forests. The new effect importance measure (EIM) associated with interaction forests allows for ranking of covariate pairs with respect to their interaction effects' importance to prediction. Using EIM, separate importance value lists for univariable effects, quantitative interaction effects, and qualitative interaction effects are obtained. In the spirit of interpretable machine learning, the bivariable split types of interaction forests target easily interpretable and communicable interaction effects. To learn about the nature of the interplay between covariates identified as interacting it is convenient to visualise their estimated bivariable influence. Functions that perform this task are provided in the R package diversityForest , which implements interaction forests. In a large-scale empirical study using 220 data sets, interaction forests tended to deliver better predictions than conventional random forests and competing random forest variants that use multivariable splitting. In a simulation study, EIM delivered considerably better rankings for the relevant quantitative and qualitative interaction effects than competing approaches. These results indicate that interaction forests are suitable tools for the challenging task of identifying and making use of easily interpretable and communicable interaction effects in predictive modelling. [ABSTRACT FROM AUTHOR]

Following the line of classification-based two-sample testing, tests based on the Random Forest classifier are proposed. The developed tests are easy to use, require almost no tuning, and are applicable for any distribution on R d. Furthermore, the built-in variable importance measure of the Random Forest gives potential insights into which variables make out the difference in distribution. An asymptotic power analysis for the proposed tests is conducted. Finally, two real-world applications illustrate the usefulness of the introduced methodology. To simplify the use of the method, the R-package "hypoRF" is provided. [ABSTRACT FROM AUTHOR]

Due to delay in reporting, the daily national and statewide COVID-19 incidence counts are often unreliable and need to be estimated from recent data. This process is known in economics as nowcasting. We describe in this paper a simple random forest statistical model for nowcasting the COVID-19 daily new infection counts based on historic data along with a set of simple covariates, such as the currently reported infection counts, day of the week, and time since first reporting. We apply the model to adjust the daily infection counts in Ohio, and show that the predictions from this simple data-driven method compare favorably both in quality and computational burden to those obtained from the state-of-the-art hierarchical Bayesian model employing a complex statistical algorithm. The interactive notebook for performing nowcasting is available online at https://tinyurl.com/simpleMLnowcasting. • Delays in reporting often make daily statewide COVID-19 incidence counts unreliable. • We developed a simple model for nowcasting the daily counts from historic data. • To calibrate the model we use historic rates of backfilling COVID-19 cases in Ohio. • On the Ohio COVID-19 dataset our method outperforms the standard model. • The algorithm is implemented in jupyter notebook environment for public use. [ABSTRACT FROM AUTHOR]

• Impact of covariates on COVID-19 response were tested via Granger-causality tests. • Climatic impact on COVID-19 cases-to-mortality ratios were studied in 36 countries. • Relationship equations were established using regression analysis. • Temperature data were factored-in in training forecasting models. • The trained models were used for forecasting COVID-19 cases-to-mortality ratios. There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor – temperature – was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries. [ABSTRACT FROM AUTHOR]

Fault prediction technology provides a way to reduce the loss caused by equipment failure. Currently, many efforts of failure prediction pay more attention to the fault trend and the remaining useful life. This paper devotes to predicting a specific failure mode of device in the future. We propose a two-step prediction method with two sub-models to predict the fault mode. The first model, named regression model, combines Attention-based-LSTM (Long Short-Term Memory) and Random Forest (RF) to predict time series trends, where wavelet packet analysis is adopted to extract elementary features of time series and form multivariable time series. Based on the status data forecasted by the first model, the second model, named classification model, combines Attention-based-LSTM and Extra-Tree (ET) to classify fault mode. The classified fault will occur in the device at some point in the future. In the regression model, Attention-based-LSTM performs deep level feature extraction of vibration sensor signals, and the result is fed to the last layer (i.e., an RF layer), which takes responsibility for predicting the future sequence. The future sequence is then provided to the classification model, where Attention-based-LSTM is used to extract implicit features, and ET classifier is used to accept these features and figure out the failure phenomenon. Our approach for predicting failure modes includes the severity and type of fault, which are rarely investigated synthetically. The experimental results of bearing data show that the sub-models can achieve high prediction accuracy and our method can predict the future failure mode with precision. [ABSTRACT FROM AUTHOR]