In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are scattered in the literature, with no common framework to describe them and to allow an objective comparison. Here, we revisit a categorization of existing multi-label classification methods and, as our main contribution, we provide a comprehensive survey and novel categorization of the feature selection techniques that have been created for the multi-label classification setting. We conclude this work with concrete suggestions for future research in multi-label feature selection which have been derived from our categorization and analysis.

Categorizing feature selection methods for multi-label classification

Graphical abstractDisplay Omitted HighlightsDifferent from the traditional multi-label feature selection, the proposed algorithm derives from different cognitive viewpoints.A simple and intuitive metric to evaluate the candidate features is proposed.The proposed algorithm is applicable to both categorical and numerical features.Our proposed method outperforms some other state-of-the-art multi-label feature selection methods in our experiments. Multi-label learning deals with data associated with a set of labels simultaneously. Like traditional single-label learning, the high-dimensionality of data is a stumbling block for multi-label learning. In this paper, we first introduce the margin of instance to granulate all instances under different labels, and three different concepts of neighborhood are defined based on different cognitive viewpoints. Based on this, we generalize neighborhood information entropy to fit multi-label learning and propose three new measures of neighborhood mutual information. It is shown that these new measures are a natural extension from single-label learning to multi-label learning. Then, we present an optimization objective function to evaluate the quality of the candidate features, which can be solved by approximating the multi-label neighborhood mutual information. Finally, extensive experiments conducted on publicly available data sets verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods.

Multi-label feature selection based on neighborhood mutual information

Distinguishing two types of labels for multi-label feature selection

Feature selection has been an important issue in machine learning and data mining, and is unavoidable when confronting with high‐dimensional data. With the advent of multilabel (ML) datasets and their vast applications, feature selection methods have been developed for dimensionality reduction and improvement of the classification performance. In this work, we provide a comprehensive review of the existing multilabel feature selection (ML‐FS) methods, and categorize these methods based on different perspectives. As feature selection and data classification are closely related to each other, we provide a review on ML learning algorithms as well. Also, to facilitate research in this field, a section is provided for setup and benchmarking that presents evaluation measures, standard datasets, and existing software for ML data. At the end of this survey, we discuss some challenges and open problems in this field that can be pursued by researchers in future. WIREs Data Mining Knowl Discov 2018, 8:e1240. doi: 10.1002/widm.1240

Multilabel feature selection: A comprehensive review and guiding experiments

Online multi-label streaming feature selection based on neighborhood rough set

Multi-label classification has gained extensive attention recently. Compared with traditional classification, multi-label classification allows one instance to associate with multiple labels. The curse of dimensionality existing in multi-label data presents a challenge to the performance of multi-label classifiers. Multi-label feature selection is a powerful tool for high-dimension problem. However, the existing feature selection methods are unable to take both computational complexity and label correlation into consideration. To address this problem, a new approach based on information gain for multi-label feather selection (IGMF) is presented in this paper. In the process of IGMF, Information gain between a feature and label set is exploited to measure the importance of the feature and label corrections. After that, the optimal feature subset are obtained by setting the threshold value. A series of experimental results show that IGMF can promote performance of multi-label classifiers.

Multi-label Feature Selection via Information Gain

To this day, the prevention of coronavirus disease is still an arduous battle. Medical imaging technology has played an important role in the fight against the epidemic. This paper is to perform feature selection on the CT image feature sets used for COVID-19 detection to improve the speed and accuracy of detection. In this work, the population-based intelligent optimization algorithm Aquila optimizer is used for feature selection. This feature selection method uses an S-shaped transfer function to process continuous values and convert them into binary form. And when the performance of the updated solution is not good, a new mutation strategy is proposed to enhance the convergence effect of the solution. Through the verification of two CT image sets, the experimental results show that the use of the S-shaped transfer function and the proposed mutation strategy can effectively improve the effect of feature selection. The prediction accuracy of the features selected by this method on the two open datasets is 99.67% and 99.28%, respectively. 

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

基于信息增益的多标签特征选择算法 (Multi-label Feature Selection Algorithm Based on Information Gain).

A nasal pattern recognition method based on multi-feature fusion is proposed to address the problems of imperfect feature information extraction, low recognition accuracy and interference of redundant information in nasal pattern images by a single method. An improved two-channel attention mechanism (I_CBAM) is introduced in the residual network to reduce the interference of redundant information; the output feature information of Layer2, Layer3 and Layer4 in the fusion depth residual network structure is used to enrich the extracted global features of the image by using the complementarity between feature maps of different scales; meanwhile, extract the underlying local features with better matching in the nasal pattern image using the improved SURF algorithm, and fuse the extracted global features with the local features; the training of the model is supervised using the improved fusion loss function. The experimental results on the nasal pattern dataset show that the recognition accuracy is improved compared with other mainstream recognition methods.

Ling Li

Papers

Multi-label Feature Selection via Information Gain

A Novel Feature Selection Algorithm Based on Aquila Optimizer for COVID-19 Classification

基于信息增益的多标签特征选择算法 (Multi-label Feature Selection Algorithm Based on Information Gain).

A multi-feature fusion approach to nasal pattern recognition