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In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a combination of over-sampling, under-sampling, and different ensemble algorithms, including Extreme Gradient Boosting (XGBoost), random forest, and bagging, is employed in order to achieve a balanced dataset and address the issues including redundancy of data after over-sampling, information loss in under-sampling, and random sample selection for sampling and sample generation. The performance of the proposed algorithm is evaluated and compared to different state-of-the-art competing algorithms based on 20 benchmark imbalanced datasets in terms of the harmonic mean of precision and recall (F1) and area under the receiver operating characteristics curve (AUC) measures. Based on the results, the proposed CSBBoost algorithm performs significantly better than the competing algorithms. In addition, a real-world dataset is used to demonstrate the applicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Classification is a widely used supervised learning technique that enables models to discover the relationship between a set of features and a specified label using available data. Its applications span various fields such as engineering, telecommunication, astronomy, and medicine. In this paper, we propose a novel rule-based classifier called RUCIB (RUle-based Classifier Inspired by BRADO), which draws inspiration from the socio-inspired swarm intelligence algorithm known as BRADO. RUCIB introduces two key aspects: the ability to accommodate multiple values for features within a rule and the capability to explore all data features simultaneously. To evaluate the performance of RUCIB, we conducted experiments using ten databases sourced from the UCI machine learning database repository. In terms of classification accuracy, we compared RUCIB to ten well-known classifiers. Our results demonstrate that, on average, RUCIB outperforms Naive Bayes, SVM, PART, Hoeffding Tree, C4.5, ID3, Random Forest, CORER, CN2, and RACER by 9.32%, 8.97%, 7.58%, 7.4%, 7.34%, 7.34%, 7.22%, 5.06%, 5.01%, and 1.92%, respectively. [ABSTRACT FROM AUTHOR]

Economic progress has been founded on environmental pressure by generating all types of environmental harm, such as an increase in greenhouse gases in the atmosphere and significant climate change, which has forced people to reflect. Building a people-oriented, comprehensive, coordinated system to compile experience and lessons assists the government in formulating appropriate policies according to the meaning of the scientific development viewpoint. Based on the above, this research designs Cost–Benefit Decision Tree (CBDT) Algorithm for the economic protection development level using a Decision Tree (DT) and Ensemble Algorithm. In the design of ML Algorithm, this research work initially introduces the Decision Tree model for economic protection evaluation by emphasizing its value as a basic machine-learning framework for fully examining many aspects of economic protection development. Second, it investigates the critical role of Ensemble Algorithms, such as Random Forest in augmenting the capabilities of DT. Third, the paper focuses on the development of a complete Economic Protection Evaluation Index System using the DT Algorithm. To provide a robust, scientifically sound, and practicable framework for evaluating economic protection, this approach integrates scientificity, comparability, systematization, hierarchy, relative decoupling, and data availability principles. Finally, the study constructs the suggested ML Algorithm using the insights from the preceding parts. This algorithm combines the Decision Tree and Random Forest models to generate a single, effective tool for evaluating economic development levels. The experimental findings suggest that the proposed hybrid assessment technique is effective across several dimensions. The proposed CBDT demonstrated its capacity to adapt to changing data circumstances in evaluating low-carbon efficiency using real-world data from 30 major Chinese cities. Furthermore, the algorithm regularly beats rival techniques regarding efficiency improvements, with an average improvement of 12.5% across all cities. Notably, it outperforms other algorithms regarding computational efficiency, with a quicker execution time and an exceptional accuracy rate of 93.2%. The proposed ML Algorithm improves decision-making in the economic protection development level field by effectively balancing cost and benefit considerations. It will improve accuracy and efficiency to outperform existing approaches, thereby advancing economic protection evaluation and decision-making processes in this domain. [ABSTRACT FROM AUTHOR]

This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features. [ABSTRACT FROM AUTHOR]

It is hard to come up with a strong learning algorithm with high cross-media retrieval accuracy, but finding a weak learning algorithm with slightly higher accuracy than random prediction is simple. This paper proposes an innovative Bagging-based cross-media retrieval algorithm (called BCMR) based on this concept. First, we use bootstrap sampling to take a random sample from the original set. The amount of sample abstracted by bootstrapping is set to be the same as the amount of sample abstracted by the original dataset. Secondly, 50 bootstrap replicates are used to train 50 weak classifiers. In our experiments, we used homogeneous individual classifiers and eight different baseline methods. Last but not least, we generate the final strong classifier from 50 weak classifiers using the sample voting integration strategy. Using collective wisdom, we can get rid of bad decisions, giving the integrated model a much better generalization ability. Extensive experiments on three datasets show that BCMR can significantly improve cross-media retrieval accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we compare the performance of two data-driven algorithms to deal with an automatic classification problem in geomorphology: Direct Sampling (DS) and Random Forest (RF). The main goal is to provide a semi-automated procedure for the geomorphological mapping of alpine environments, using a manually mapped zone as training dataset and predictor variables to infer the classification of a target zone. The applicability of DS to geomorphological classification was never investigated before. Instead, RF based classification has already been applied in few studies, but only with a limited number of geomorphological classes. The outcomes of both approaches are validated by comparing the eight detected classes with a geomorphological map elaborated on the field and considered as ground truth. Both DS and RF give satisfactory results and provide similar performances in term of accuracy and Cohen's Kappa values. The map obtained with RF presents a noisier spatial distribution of classes than when using DS, because DS takes into account the spatial dependence of the different classes. Results suggest that DS and RF are both suitable techniques for the semi-automated geomorphological mapping in alpine environments at regional scale, opening the way for further improvements. [ABSTRACT FROM AUTHOR]

Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers' health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia. [ABSTRACT FROM AUTHOR]

The insulating strength of an air gap is an important basis for external insulation design of power transmission and transformation project, and both gap structure and meteorological conditions have a great influence on the breakdown voltage of air gap. The temperature, humidity and other meteorological conditions in the actual operating environment of power transmission and transformation projects may change greatly with time, so presently, there is no better method to calculate the breakdown voltage of air gaps in various meteorological conditions. In this paper, a prediction model for positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees is proposed. All samples are divided into training set and test set by Self-Organizing Mapping neural network and stratified sampling method. In the test set, the predicted breakdown voltage results of rod-plane air gap based on decision trees, random forest, extremely randomized trees and support vector machine are compared, with reference to the rectification results of atmospheric correction method. The result shows that the maximum absolute relative error of the model is 4.3% and the mean absolute percentage error is 1.6%. Then, the importance of features of import variables is calculated with the trained extremely randomized trees model. Finally, the breakdown voltage of rod-plane air gap in correspondence to different gap distances in four extreme meteorological conditions is calculated with the model. The method can provide reference for calculation of the breakdown voltage of air gap in various meteorological conditions. [ABSTRACT FROM AUTHOR]

Prediction of rockburst in underground engineering is of significance as it is close to the safety of support structures, personnel, and working environments. To improve the accuracy of rockburst classification, this paper proposed an ensemble classifier RF-FA model in which the random forest classifier (RF) and firefly algorithm (FA) were combined to achieve the optimum performance on rockburst prediction. Five key parameters of surrounding rock, i.e., the depth H, the maximum tangential stress σθ, the uniaxial compressive strength σc, the tensile strength σt, and the elastic energy index Wet, are selected as input variables while the rockburst intensity including none, light, moderate, and strong classes was chosen as output. A total of 279 cases worldwide were collected and used for train and test the proposed RF-FA model. The results showed that the FA can optimize the hyperparameters of RF efficiently and the optimum model exhibited a high performance on rockburst data from the independent test set and new engineering projects. The feature importance obtained by the ensemble RF-FA model indicated that the elastic energy index plays the most important role in rockburst. Besides, the proposed model showed much better accuracy on rockburst classification compared with previously existing RF models and empirical criteria, which means it is a useful and robust tool for rockburst prediction. [ABSTRACT FROM AUTHOR]

Solar flares are one of the most important sources of disastrous space weather events, leading to negative effects on spacecrafts and living organisms. It is very important to predict solar flares to minimize the potential losses. In this paper, we use three different machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost (XGB) to predict the total flare index T flare and the maximum flare index M flare of an active region (AR) within the subsequent of 24, 48, and 72 hrs. First, we selected 54514 vector magnetograms of 129 ARs on the visible solar hemisphere in solar cycle 24 whose maximum sunspot groups' area was larger than 400 μh. Then the following four magnetic parameters of each magnetogram were calculated: 1) the total magnetic flux | Φ tot | , 2) the total photospheric free magnetic energy density E free , 3) the gradient-weighted integral length of the neutral line with horizontal magnetic gradient of line-of-sight magnetic field larger than 0.1 G km − 1 ( WL SG ), and 4) the area with magnetic shear angle larger than 40 ∘ ( A Ψ ), as well as T flare and M flare corresponding to each magnetogram. Afterward, we split samples randomly into training (85% of the whole data) and testing (15%) data sets. After hyperparameter tuning and model construction we found that RF is an optimal algorithm for the prediction task and that the coefficients of determination ( R 2 ) of test data set via the majority of RF models are beyond 0.97. In addition, the feature importance of RF and XGB models indicates that | Φ tot | and E free are two optimal parameters to predict both T flare and M flare , and | Φ tot | and E free are the best parameters for M flare and T flare , respectively. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]