Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems

Unsupervised attribute reduction based on variable precision weighted neighborhood dependency

Neighborhood rough set (NRS) have been successfully applied to attribute reduction (AR). However, most current methods of AR based on NRS are supervised or semi-supervised. This limits their ability to process data without decision information. When granulating data samples, NRS considers only the number of samples within the neighborhood radius. It does not consider distribution information between samples, which can result in the loss of original data information. To address the aforementioned issue, we propose an unsupervised attribute reduction (UAR) strategy based on variable precision weighted neighborhood dependency (VPWND) (UAR_VPWND). We compare algorithm UAR_VPWND to existing classical UAR algorithms using public datasets. The experimental results show that algorithm UAR_VPWND can select fewer attributes to maintain or improve the performance of clustering learning algorithms.

A Building Extraction Method for High-Resolution Remote Sensing Images with Multiple Attentions and Parallel Encoders Combining Enhanced Spectral Information

Accurately extracting pixel-level buildings from high-resolution remote sensing images is significant for various geographical information applications. Influenced by different natural, cultural, and social development levels, buildings may vary in shape and distribution, making it difficult for the network to maintain a stable segmentation effect of buildings in different areas of the image. In addition, the complex spectra of features in remote sensing images can affect the extracted details of multi-scale buildings in different ways. To this end, this study selects parts of Xi'an City, Shaanxi Province, China, as the study area. A parallel encoded building extraction network (MARS-Net) incorporating multiple attention mechanisms is proposed. MARS-Net builds its parallel encoder through DCNN and transformer to take advantage of their extraction of local and global features. According to the different depth positions of the network, coordinate attention (CA) and convolutional block attention module (CBAM) are introduced to bridge the encoder and decoder to retain richer spatial and semantic information during the encoding process, and adding the dense atrous spatial pyramid pooling (DenseASPP) captures multi-scale contextual information during the upsampling of the layers of the decoder. In addition, a spectral information enhancement module (SIEM) is designed in this study. SIEM further enhances building segmentation by blending and enhancing multi-band building information with relationships between bands. The experimental results show that MARS-Net performs better extraction results and obtains more effective enhancement after adding SIEM. The IoU on the self-built Xi'an and WHU building datasets are 87.53% and 89.62%, respectively, while the respective F1 scores are 93.34% and 94.52%.

Multi-label feature selection based on fuzzy neighborhood rough sets

Multi-label feature selection, a crucial preprocessing step for multi-label classification, has been widely applied to data mining, artificial intelligence and other fields. However, most of the existing multi-label feature selection methods for dealing with mixed data have the following problems: (1) These methods rarely consider the importance of features from multiple perspectives, which analyzes features not comprehensive enough. (2) These methods select feature subsets according to the positive region, while ignoring the uncertainty implied by the upper approximation. To address these problems, a multi-label feature selection method based on fuzzy neighborhood rough set is developed in this article. First, the fuzzy neighborhood approximation accuracy and fuzzy decision are defined in the fuzzy neighborhood rough set model, and a new multi-label fuzzy neighborhood conditional entropy is designed. Second, a mixed measure is proposed by combining the fuzzy neighborhood conditional entropy from information view with the approximate accuracy of fuzzy neighborhood from algebra view, to evaluate the importance of features from different views. Finally, a forward multi-label feature selection algorithm is proposed for removing redundant features and decrease the complexity of multi-label classification. The experimental results illustrate the validity and stability of the proposed algorithm in multi-label fuzzy neighborhood decision systems, when compared with related methods on ten multi-label datasets.

Nonlinear Random Forest Classification, a Copula-Based Approach

In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.

Feature Selection Combining Information Theory View and Algebraic View in the Neighborhood Decision System

Feature selection is one of the core contents of rough set theory and application. Since the reduction ability and classification performance of many feature selection algorithms based on rough set theory and its extensions are not ideal, this paper proposes a feature selection algorithm that combines the information theory view and algebraic view in the neighborhood decision system. First, the neighborhood relationship in the neighborhood rough set model is used to retain the classification information of continuous data, to study some uncertainty measures of neighborhood information entropy. Second, to fully reflect the decision ability and classification performance of the neighborhood system, the neighborhood credibility and neighborhood coverage are defined and introduced into the neighborhood joint entropy. Third, a feature selection algorithm based on neighborhood joint entropy is designed, which improves the disadvantage that most feature selection algorithms only consider information theory definition or algebraic definition. Finally, experiments and statistical analyses on nine data sets prove that the algorithm can effectively select the optimal feature subset, and the selection result can maintain or improve the classification performance of the data set.

Novel Prediction Model for Steel Mechanical Properties with MSVR Based on MIC and Complex Network Clustering

Traditional mechanical properties prediction models are mostly based on experience and mechanism, which neglect the linear and nonlinear relationships between process parameters. Aiming at the high-dimensional data collected in the complex industrial process of steel production, a new prediction model is proposed. The multidimensional support vector regression (MSVR)-based model is combined with the feature selection method, which involves maximum information coefficient (MIC) correlation characterization and complex network clustering. Firstly, MIC is used to measure the correlation between process parameters and mechanical properties, based on which a complex network is constructed and hierarchical clustering is performed. Secondly, we evaluate all parameters and select a representative one for each partition as the input of the subsequent model based on the centrality and influence indicators. Finally, an actual steel production case is used to train the MSVR prediction model. The prediction results show that our proposed framework can capture effective features from the full parameters in terms of higher prediction accuracy and is less time-consuming compared with the Pearson-based subset, full-parameter subset, and empirical subset input. The feature selection method based on MIC can dig out some nonlinear relationships which cannot be found by Pearson coefficient.