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This paper introduces a novel feature selection method, called Feature Selection based on Importance Measures (FS-IM), to enhance the forecasting of crude oil returns. FS-IM innovatively combines active learning with the application of Gaussian noise to input features and selects the most relevant features using an optimal threshold value. The paper applies a ridge regression (RR) model based on FS-IM (FS-RR) to identify the factors that have important information for crude oil return forecasting. The paper compares FS-IM with other dimension reduction methods such as Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), and Independent Component Analysis (ICA). The results show that FS-IM can significantly improve model accuracy, demonstrating its effectiveness in finding key features. Moreover, FS-IM is more stable and consistent than other dimension reduction methods in enhancing the prediction accuracy in different scenarios, indicating its superior capability in capturing complex relationships between input and output variables. Furthermore, this study compares FS-RR model with other 13 prediction models by conducting experiments using a series of evaluation metrics, different statistical tests, and different step-ahead predictions and training sets. The results confirm that the RR model based on FS-IM can consistently outperform other model in terms of predictive performance and economic value, proving its effectiveness and robustness. This study contributes to the literature on crude oil price forecasting by addressing the challenges of high-dimensional and complex data, and by providing a robust, practical tool for professionals in energy economics and finance. • A new feature selection method for crude oil forecasting using importance scores and Gaussian noise is proposed. • The method selects the most relevant features based on the prediction errors of a base forecasting model. • The method enhances the accuracy and value of ridge regression model compared to other methods. • The method handles high-dimensional and complex data without losing information or interpretability. [ABSTRACT FROM AUTHOR]

In numerous domains, there is ample evidence indicating that a significant portion of real-world data exhibits temporal variations, such as medical research and meteorological studies. Particularly in the era of big data, not only does the size of data change dynamically but also its dimensions at an unprecedented pace. Consequently, employing traditional methods to handle such dynamic data becomes highly impractical. To address this limitation, this paper proposes an incremental feature selection algorithm tailored for dynamic feature variations. For scenarios involving an increase in features, the novel algorithm efficiently identifies a target subset with effective features. In order to showcase the efficacy of the proposed algorithm, this paper conduct experiments using four commonly used classifiers and five UCI data sets. The experimental results further validate both the feasibility and efficiency of the new approach. [ABSTRACT FROM AUTHOR]

Existing supervised Multi-Target Feature Selection (MTFS) methods seldom consider the nearest-neighbor relationship and statistical correlation of samples underlying the output space, which leads the result of feature selection to be easily interfered by the output noise, thus making it difficult to achieve satisfactory performance. This paper proposes a novel MTFS method to preserve both the global and local target correlations. Specifically, the low-rank constraint is introduced to achieve multi-layer regression structure to better decouple the inter-input and inter-target relationships. Moreover, the local nearest-neighbor relationships and variable correlations of the sample points in the output space are also explored through adaptive graph and manifold learning, to better utilize the target correlations to improve the MTFS performance. Following the above principle, the resulting objective function and the corresponding optimization algorithm are proposed. Extensive experiments on several public datasets show that the proposed method is superior to other state-of-the-art methods. • A new Embedded MTFS method is proposed to learn the feature redundancy and target correlations in MTR problems. • The nearest-neighbor relationships and target correlations are introduced to achieve superior performance in MTFS tasks. • Extensive experiments on several public datasets show that the proposed method is superior to the compared methods. [ABSTRACT FROM AUTHOR]

Intrusion detection technology can actively detect abnormal behaviors in the network and is important to the security of Industrial Internet of Things (IIOT). However, there are some issues with the current intrusion detection technology for IIOT, such as extreme imbalance in the number of samples of different classes in the dataset, redundant and meaningless features in the samples, and the inability of traditional intrusion detection methods to meet the requirements of detection accuracy in the increasingly complex IIOT. In view of the extreme imbalance of classes, this paper applies the hierarchical clustering algorithm to the under-sampling technology, which reduces the number of majority samples while reducing the loss of information of majority samples, and solves the problem of missing detection and false detection of minority samples caused by sample imbalance. In order to avoid feature redundancy and interference, this paper proposes an optimal feature selection algorithm based on greedy thought. This algorithm can obtain the optimal feature subset of each type of data in the data set, thus eliminating redundant and interfering features. Aiming at the problem of insufficient detection ability of traditional detection methods, this paper proposes a deep neural network intrusion detection model based on the parallel connection of global and local subnetworks. This model obtains the overall benchmark detection of the dataset through the deep neural network, and then strengthens the detection effect of each subclass through the parallel connection of subnetworks, greatly improving the performance of the intrusion detection algorithm. The experimental results show that the method described in this paper can improve the intrusion detection for IIOT. [ABSTRACT FROM AUTHOR]

In hierarchical classification learning, the hierarchical feature selection algorithm plays an important role in overcoming the curse of dimensionality. Existing hierarchical feature selection algorithms, based on the granular computing framework, all use three basic search strategies to search for similar and dissimilar classes. These strategies compute the importance of features to the global label for feature selection. However, existing methods based on the sibling strategy can only stay at the fine-grained level for feature selection, often without considering that the fine-grained level is also continuously separated from the coarse-grained level. Thus, these methods do not take into account the features hidden below the coarse granularity, resulting in the selection of a top-heavy subset of features and the loss of many important features. Therefore, this paper proposes a Hierarchical Feature Selection Based on Neighborhood Interclass Spacing From Fine to Coarse (HFSNIS) algorithm, which aims to change the feature selection to the coarse-grained hierarchy. The framework of the HFSNIS algorithm is as follows: First, each fine-grained leaf node is coarsened to the coarsest hierarchy of granularity from fine to coarse, where the non-root ancestor node is located. Next, the search for similar and dissimilar nearest neighbors is performed at the coarsest granularity hierarchy. Finally, the features are filtered using the Neighborhood Interclass Spacing model to obtain a subset of features. Therefore, this HFSNIS algorithm based on the Coarsest Search Strategy (CSS) can reselect features that were previously ignored in the fine-grained hierarchy, resulting in a better feature subset. Finally, the proposed algorithm outperforms seven state-of-the-art feature selection algorithms on six datasets. [ABSTRACT FROM AUTHOR]

In recent years, Unsupervised Feature Selection (UFS) methods have attracted considerable interest in different research areas due to their wide application in problems where unlabeled data appears. Nevertheless, few UFS methods can process datasets described by both numerical and non-numerical features (mixed data). This paper introduces a new filter UFS method based on a new feature correlation measure for mixed data to select a relevant and non-redundant feature subset. The proposed method addresses the feature selection problem in two stages through a strategy that combines Spectral Feature Selection to identify relevant features and a Pair-wise Redundancy Analysis to remove those features with a high correlation with others. According to our experiments, the proposed feature selection method based on the introduced feature correlation measure provides better quality results than previous filter UFS methods for mixed data reported in the literature. • A filter Unsupervised Feature Selection method for mixed data is presented. • A new feature correlation measure for mixed data is introduced. • Spectral Feature Selection and Pair-wise Redundancy Analysis were applied. • A comprehensive evaluation of the proposed method is provided. [ABSTRACT FROM AUTHOR]

The number of fuzzy rules in a fuzzy neural network usually depends on the grid-type partition or clustering-based partition. However, the number of grids or clusters needs to be set in advance and the initial number of fuzzy sets on each dimension is usually the same. This usually does not fit the real situation (for example, when forecasting the weather, various features such as temperature, humidity, wind direction, wind speed, etc., may have different numbers of fuzzy sets). In this paper, a novel zero-order fuzzy neural network with an adaptive fuzzy partition (AdFPFNN) is proposed, which does not need to preset the number of fuzzy sets and owns various numbers of fuzzy sets on different dimensions. The trick is to use the mean shift algorithm to perform adaptive clustering for each feature individually. For common fuzzy neural networks, dealing with high-dimensional problems is challenging work on account of the "fuzzy rule curse" and "computation underflow". To overcome these issues, an improved version of AdFPFNN is devised to tackle high-dimensional problems, called PaCoAMF-based AdFPFNN. It adopts two techniques to overcome the "fuzzy rule curse" and "computation underflow". One is the construction of a partially combined fuzzy rule base, which can generate rules that are adequate but do not grow exponentially with the number of features. Another is the proposal of an adaptive membership function that can guarantee the elimination of numerical underflow due to the product T-norm. Without feature selection or other dimensional reduction methods, the proposed PaCoAMF-based AdFPFNN can be directly used to solve high-dimensional problems. Simulation results on one constructed dataset and 13 real-world datasets (including 5 high-dimensional problems) confirm the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

Supervised learning-based code smell detection has become one of the dominant approaches to identify code smell. Existing works optimize the process of code smell detection from multiple aspects, such as high-quality dataset, feature selection, and model, etc. Although the accuracy is improved continuously, researchers are confused about what model are the most suitable ones to detect code smell when considering dataset construction and feature selection. Furthermore, existing surveys for code smell mainly analyze the impact of code smell, categorize the concerns of code smell, and repair code smell. There is a lack of systematic analysis and classification of code smell detection based on supervised learning. To this end, we collect 86 papers of code smell detection based on supervised learning ranging from January 2010 to April 2023. A total of 7 research questions is empirically evaluated from different aspects, such as datasets construction, data pre-processing, feature selection, and model training, etc. We conclude that existing works suffer from issues such as sample imbalance, different attention to types of code smell, and limited feature selection. Finally, we suggest possible future research directions. [ABSTRACT FROM AUTHOR]

Feature selection is a vital preprocessing step in real applications of data mining and machine learning. With the prevalence of high-dimensional hybrid data sets in real-world scenarios, along with the presence of test costs and misclassification costs, the need for effective feature selection methods has become more prominent. However, existing feature selection approaches mainly focus on cost-sensitive data from a single granularity perspective, and they are primarily applicable to single-typed data sets. To address these limitations, this paper presents a novel feature selection approach specifically designed for hybrid data, considering variable test costs and misclassification costs. The proposed method is based on neighborhood multigranulation rough sets (NMRS), which provides more comprehensive and multi-angle data analysis for cost-sensitive hybrid data. First, a novel multigranulation model is developed to effectively process cost-sensitive hybrid data. Building upon this model, a cost-based multi-criteria measure is proposed to evaluate the significance of features. This measure takes into account the comprehensive information of candidate features, including their power in algebraic view, information view, and associated costs. Furthermore, a heuristic feature selection algorithm based on NMRS is proposed to handle hybrid data with the test costs and misclassification costs. This algorithm leverages the benefits of the proposed multigranulation model and cost-based measure to identify the most discriminative features efficiently. Finally, the experimental results on twelve different datasets show that the proposed heuristic algorithm outperforms other compared algorithms, especially in total cost and classification accuracy. [ABSTRACT FROM AUTHOR]