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In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the "dual-carbon" goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system. [ABSTRACT FROM AUTHOR]

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models. [ABSTRACT FROM AUTHOR]

SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions. [ABSTRACT FROM AUTHOR]

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Background: Cancer diagnosis based on machine learning has become a popular application direction. Support vector machine (SVM), as a classical machine learning algorithm, has been widely used in cancer diagnosis because of its advantages in high-dimensional and small sample data. However, due to the high-dimensional feature space and high feature redundancy of gene expression data, SVM faces the problem of poor classification effect when dealing with such data. Methods: Based on this, this paper proposes a hybrid feature selection algorithm combining information gain and grouping particle swarm optimization (IG-GPSO). The algorithm firstly calculates the information gain values of the features and ranks them in descending order according to the value. Then, ranked features are grouped according to the information index, so that the features in the group are close, and the features outside the group are sparse. Finally, grouped features are searched using grouping PSO and evaluated according to in-group and out-group. Results: Experimental results show that the average accuracy (ACC) of the SVM on the feature subset selected by the IG-GPSO is 98.50%, which is significantly better than the traditional feature selection algorithm. Compared with KNN, the classification effect of the feature subset selected by the IG-GPSO is still optimal. In addition, the results of multiple comparison tests show that the feature selection effect of the IG-GPSO is significantly better than that of traditional feature selection algorithms. Conclusion: The feature subset selected by IG-GPSO not only has the best classification effect, but also has the least feature scale (FS). More importantly, the IG-GPSO significantly improves the ACC of SVM in cancer diagnostic. [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

For the feature selection of network intrusion detection, the issue of numerous redundant features arises, posing challenges in enhancing detection accuracy and adversely affecting overall performance to some extent. Artificial rabbits optimization (ARO) is capable of reducing redundant features and can be applied for the feature selection of network intrusion detection. The ARO exhibits a slow iteration speed in the exploration phase of the population and is prone to an iterative stagnation condition in the exploitation phase, which hinders its ability to deliver outstanding performance in the aforementioned problems. First, to enhance the global exploration capabilities further, the thinking of ARO incorporates the mud ring feeding strategy from the bottlenose dolphin optimizer (BDO). Simultaneously, for adjusting the exploration and exploitation phases, the ARO employs an adaptive switching mechanism. Second, to avoid the original algorithm getting trapped in the local optimum during the local exploitation phase, the levy flight strategy is adopted. Lastly, the dynamic lens-imaging strategy is introduced to enhance population variety and facilitate escape from the local optimum. Then, this paper proposes a modified ARO, namely LBARO, a hybrid algorithm that combines BDO and ARO, for feature selection in the network intrusion detection model. The LBARO is first empirically evaluated to comprehensively demonstrate the superiority of the proposed algorithm, using 8 benchmark test functions and 4 UCI datasets. Subsequently, the LBARO is integrated into the feature selection process of the network intrusion detection model for classification experimental validation. This integration is validated utilizing the NSL-KDD, UNSW NB-15, and InSDN datasets, respectively. Experimental results indicate that the proposed model based on LBARO successfully reduces redundant characteristics while enhancing the classification capabilities of network intrusion detection. [ABSTRACT FROM AUTHOR]

When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper explores the high-dimensional data from both the data feature dimension and the model ensemble dimension. We propose a high-dimensional ensemble learning classification algorithm focusing on feature space reconstruction and classifier ensemble, called the HDELC algorithm. First, the algorithm considers feature space reconstruction and then generates a feature space reconstruction matrix. It effectively achieves feature selection and reconstruction for high-dimensional data. An optimal feature space is generated for the subsequent ensemble of the classifier, which enhances the representativeness of the feature space. Second, we recursively determine the number of classifiers and the number of feature subspaces in the ensemble model. Different classifiers in the ensemble system are assigned mutually exclusive non-intersecting feature subspaces for model training. The experimental results show that the HDELC algorithm has advantages compared with most high-dimensional datasets due to its more efficient feature space ensemble capability and relatively reliable ensemble operation performance. The HDELC algorithm makes it possible to solve the classification problem for high-dimensional data effectively and has vital research and application value. [ABSTRACT FROM AUTHOR]

In the era of big data, the demand for multivariate time series prediction has surged, drawing increased attention to feature selection and neural networks in machine learning. However, certain feature selection methods neglect the alignment between actual data sample differences and clustering results, while neural networks lack automatic parameter adjustment in response to changing target features. This paper presents the MBSSA-Bi-AESN model, a Bi-directional Adaptive Echo State Network that utilizes the modified salp swarm algorithm (MBSSA) and feature selection to address the limitations of manually set parameters. Initial feature subset selection involves assigning weights based on the consistency of clustering results with differences. Subsequently, the four critical parameters in the Bi-AESN model are optimized using MBSSA. The optimized Bi-AESN model and selected feature subset are then integrated for simultaneous model learning and optimal feature subset selection. Experimental analysis on eight datasets demonstrates the superior prediction accuracy of the MBSSA-Bi-AESN model compared to benchmark models, underscoring its feasibility, validity, and universality. [ABSTRACT FROM AUTHOR]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]

• A feature selection method based on binary dandelion algorithm is proposed. • The algorithm applies a sine-cosine operator and a restart strategy. • Mutual information and quick bit mutation improve the performance of the algorithm. • The algorithm performs well on datasets with larger dimensions. Feature selection (FS) is an important data preprocessing technology for machine learning and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection because of its powerful search function. This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. First, the sine cosine operator is used in the radius formula of the core dandelions (CD), which significantly enhances the ability of algorithm development and exploration. Secondly, the algorithm uses a restart strategy to increase its ability to get rid of local optimum. Thirdly, mutual information is used to guide the generation of some dandelions, which pays more attention to the correlation between the selected features and categories. Finally, quick bit mutation is used as the mutation strategy to improve the diversity of the population. The SCRBDA proposed in this paper was tested on 18 datasets of different sizes from UCI machine learning database. The SCRBDA was compared with 8 other classical feature selection algorithms, and the performance of the proposed algorithm was evaluated through feature subset size, classification accuracy, fitness value, and F1-score. The experimental results show that SCRBDA achieves the best performance, which has stronger feature reduction ability and achieves better overall performance on most datasets. Especially on large-scale datasets, SCRBDA can obtain extremely smaller feature subsets while maintaining much higher classification accuracy, and satisfactory F1-score. [ABSTRACT FROM AUTHOR]

• MMODE is developed as a new hybrid gene selection method for tumor identification. • MMODE combines multi-tasking and multi-objective frameworks. • MMODE uses a new elite strategy and a new guidance strategy. • MMODE selects a few genes and achieves high classification accuracy. A key preprocessing step in tumor recognition based on microarray expression profile data and machine learning is to identify tumor marker genes. Gene selection aims to select the most relevant gene subset from the original ultra-high dimensional microarray expression profile data to improve tumor identification performance. Inspired by evolutionary multitasking (EMT) and multi-objective optimization, this paper puts forward a novel multitasking multi-objective differential evolution gene selection algorithm (MMODE) which uses new elite and guidance strategies to select the best gene subsets. MMODE initializes two different populations according to different filtering criteria to increase the diversity of the search. These two populations guide their respective populations to search in the optimal direction through knowledge transfer in the evolutionary process. In addition, MMODE employs new elite and guidance strategies that enables individuals to narrow the search range and jump out of local optima. The proposed algorithm is validated on 13 publicly available microarray expression datasets in comparison with state-of-the-art gene selection algorithms. The experimental results show that MMODE can find smaller gene subsets and achieve higher classification accuracy compared with other algorithms. [ABSTRACT FROM AUTHOR]

Attribute reduction can remove data noise and redundancy, thus reducing computational complexity, which is very important for machine learning. Because the difference between nominal attribute values is difficult to measure, attribute reduction for hybrid data faces challenges. In addition, most of the existing methods are sensitive to noise due to the lack of an anti-noise mechanism. Decision attribute contains the most important information of data. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. First of all, a new distance incorporating decision attributes is defined, and then a new fuzzy relation with an anti-noise mechanism is defined. Furthermore, fuzzy belief and fuzzy plausibility are defined based on the defined new distance and new fuzzy relation. In this framework, two anti-noise attribute reduction algorithms for hybrid data are proposed. Experiments on 12 data sets of various types show that compared with the other 8 state-of-the-art algorithms, the proposed algorithms improve the classification accuracy by at least 2% and the anti-noise ability by at least 11%. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. [ABSTRACT FROM AUTHOR]

The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in detail: finding FR methods that are compatible with wide data and the need for a reduction estimator of nonlinear approaches to process out-of-sample data. The experimental study compares 17 techniques, including supervised, unsupervised, linear, and nonlinear approaches, using 7 resampling strategies and 5 classifiers. The results demonstrate which configurations are optimal, according to their performance and computation time. Moreover, the best configuration—namely, k Nearest Neighbor (KNN) + the Maximal Margin Criterion (MMC) feature reducer with no resampling—is shown to outperform state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This research introduces innovative features tailored to capture distinctive characteristics of ransomware activity within the cryptocurrency ecosystem. The study employs a multifaceted analysis to delve into ransomware-related data encompassing transaction metadata, ransom analysis, behavioral patterns, and financial aspects. A feature selection algorithm is explored to discern ransomware transactions in Bitcoin (BTC) and the United States Dollar (USD) using the UGRansome dataset. This comprehensive dataset of ransomware-related transactions facilitates the proposal of novel features designed to capture the unique traits of ransomware activity. The correlation matrix and temporal analysis of these features contribute to a nuanced understanding of the dynamic nature of ransomware threats. The research presents the Ransomware Feature Selection Algorithm (RFSA) based on Gini Impurity and Mutual Information (MI) to effectively select crucial ransomware features. Evaluation metrics such as precision, recall, accuracy, and F1 score highlight the effectiveness of the RFSA. The analysis reveals that approximately 68% of ransomware incidents involve BTC transactions ranging from 1.46 to 2.56, with an average of 2.01 BTC transactions per attack. Moreover, ransomware causes financial damages ranging from 4.38 to 172.36 USD, with an average damage of 88.37 USD. The RFSA identifies 17 ransomware types and their associated malware to shed light on their characteristics. The study investigates the pricing of ransomware and reveals that TowerWeb is associated with the highest fee, amounting to 135.26 BTC, while CryptoLocker has the lowest fee, recorded at 10.51 BTC. Additionally, the impact of ransomware duration on financial gains and network flow is investigated, disclosing a correlation between extended duration and higher financial gains. The research achieves outstanding performance metrics, including an MI score of 95%, accuracy of 93%, recall of 92%, and precision of 89%. These results showcase the superiority of the proposed approach over existing studies, emphasizing the dynamic and adaptable nature of ransomware demands. The findings suggest that there is no fixed amount for specific cyberattacks. This underscores the importance of adapting to the evolving landscape of ransomware threats. [ABSTRACT FROM AUTHOR]