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This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field.

This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field. [ABSTRACT FROM AUTHOR]

Aiming at the problem of poor detection performance under the environment of imbalanced type distribution, an intrusion detection model of genetic algorithm to optimize weighted extreme learning machine based on stratified cross-validation (SCV-GA-WELM) is proposed. In order to solve the problem of imbalanced data types in cross-validation subsets, SCV is used to ensure that the data distribution in all subsets is consistent, thus avoiding model over-fitting. The traditional fitness function cannot solve the problem of small sample classification well. By designing a weighted fitness function and giving high weight to small sample data, the performance of the model can be effectively improved in the environment of imbalanced type distribution. The experimental results show that this model is superior to other intrusion detection models in recall and McNemar hypothesis test. In addition, the recall of the model for small sample data is higher, reaching 91.5% and 95.1%, respectively. This shows that it can effectively detect intrusions in an environment with imbalanced type distribution. Therefore, the model has practical application value in the field of intrusion detection, and can be used to improve the performance of intrusion detection systems in the actual environment. This method has a wide application prospect, such as network security, industrial control system, and power system. [ABSTRACT FROM AUTHOR]

Flatfoot is a common disease in children and juveniles. If the disease is not controlled and treated in time, it may last into adulthood, which can bring a great deal of inconvenience and even pain to daily life. In addition to the diagnosis of the disease simply by doctors and medical equipment, artificial intelligence has become a very promising auxiliary diagnostic tool. In this paper, a neural network with a simple structure is used to classify the foot data to achieve the function of diagnosing flatfoot. The presented neural network is termed as modified weights-and-structure-determination neural network (MWASDNN), of which the input weights are analytically determined by the pseudo-inverse method, while the output weights are randomly generated within a specified interval, and the number of hidden-layer neurons is determined by an incremental method. In addition, the stratified cross-validation method is introduced to choose the model structure that best fits the features of the data set, thereby improving the generalization performance and robustness of the MWASDNN. Utilizing the MWASDNN models to classify the foot data we collected, we finally get the accuracy of 84.31% and 85.29% on the left and right foot data, respectively. Besides, MWASDNN achieves the highest classification accuracy on our foot data set compared to some traditional neural networks, pattern classification methods, and two improved neural networks. These excellent results indicate that the MWASDNN is expected to be designed as a practical flatfoot diagnostic tool.

With the global expansion of internet businesses, companies look for possibilities to increase their turnovers in virtual media. In these circumstances, it is significant for companies to employ online user behavior data in order to understand user intents and, to some extent, influence user online activity. This research examines the practical solution of the stated goal through an application of deep learning models. The contribution of the current study is an investigation of the success of real-time influence on web portal users to conduct specific activities leading to the achievement of predefined business goals. The study investigates several top-rated over-sampling techniques while solving class imbalance problem to evaluate their impact on the deep learning model performance. The study was conducted on the Lithuanian online educational platform. The obtained results indicate the feasibility of the suggested method. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the use of a new Measure–Correlate–Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R 2) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site. [Copyright &y& Elsevier]