A Parallel Coordinates Plot Method Based on Unsupervised Feature Selection for High-Dimensional Data Visualization

The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the "curse of dimensionality" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems. The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients. We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis. In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives. This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

Over the past decades, parallel coordinates plots (PCPs) have been extensively applied to various tasks of multivariate data visualization. However, a main disadvantage of PCPs is the almost inevitable overlap of polylines, which causes chaotic visual effects when the number of polylines is large and the sequence of parallel coordinates is not conducive to visualization. To tackle this issue, this paper proposed an axis permutation of PCPs based on Laplacian scores (LS), which can be used to enhance the visualization performance of the original PCPs. The enhanced PCP is a combination of an unsupervised feature selection technique (LS) and the visualization of multivariate data. With the new sequence of axes based on the Laplacian scores of the corresponding features, polylines of PCPs are distributed with less clutter and overlap. The experimental results demonstrated that the proposed method made great progress in enhancing the visualization performance of PCPs.

In many real-world unsupervised learning applications, given data with balanced distribution, that is, there are an approximately equal number of instances in each class, we often need to construct a model to reveal such balance. However, in many data, especially the high-dimensional ones, the data in the original feature space often do not present such balance due to the redundant and noisy features. To tackle this problem, we apply an unsupervised spectral feature selection method to select some informative features, which can better reveal the balanced structure of data. Although spectral feature selection is one of the most popular unsupervised feature selection methods and has been widely studied, none of the existing spectral feature selection methods consider the balance property of data. To address this issue, in this article, we propose a novel balanced spectral feature selection (BSFS) method, which not only selects the discriminative features but also picks those to reveal the balanced structure of data. To the best of our knowledge, this is the first spectral feature selection method considering balance structure of data. By introducing a balanced regularization term, we integrate the balanced spectral clustering and feature selection into a unified framework seamlessly. At last, the experiments on benchmark datasets show that the proposed one outperforms the conventional feature selection methods in both clustering performance and balance, which demonstrates the effectiveness and efficiency of the proposed method.

Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods.

Graph self-representation method for unsupervised feature selection

Neurodynamics-driven holistic approaches to semi-supervised feature selection

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Background: Dimension disaster is often associated with feature extraction. The extracted features may contain more redundant feature information, which leads to the limitation of computing ability and overfitting problems. Objective: Feature selection is an important strategy to overcome the problems from dimension disaster. In most machine learning tasks, features determine the upper limit of the model performance. Therefore, more and more feature selection methods should be developed to optimize redundant features. Methods: In this paper, we introduce a new technique to optimize sequence features based on the binomial distribution (BD). Firstly, the principle of the binomial distribution algorithm is introduced in detail. Then, the proposed algorithm is compared with other commonly used feature selection methods on three different types of datasets by using a Random Forest classifier with the same parameters. Results: The results confirm that BD has a promising improvement in feature selection and classification accuracy. Conclusion: Finally, we provide the source code and executable program package (http: //lingroup. cn/server/BDselect/), by which users can easily perform our algorithm in their researches.

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS.

An unsupervised feature selection algorithm based on ant colony optimization

Sparse feature selection: Relevance, redundancy and locality structure preserving guided by pairwise constraints

Compared to supervised feature selection, unsupervised feature selection tends to be more challenging due to the lack of guidance from class labels. Along with the increasing variety of data sources, many datasets are also equipped with certain side information of heterogeneous structure. Such side information can be critical for feature selection when class labels are unavailable. In this paper, we propose a new feature selection method, SideFS, to exploit such rich side information. We model the complex side information as a heterogeneous network and derive instance correlations to guide subsequent feature selection. Representations are learned from the side information network and the feature selection is performed in a unified framework. Experimental results show that the proposed method can effectively enhance the quality of selected features by incorporating heterogeneous side information.

Feature selection is an important pre-processing stage in many machine learning and pattern recognition tasks, which eliminates irrelevant and redundant features and improves learning performance. Regularized sparse feature selection methods like Lasso and its variants using l 1 -norm regularization term in their optimization problem have received much attention in recent years. Prior information could be represented as the class labels or pairwise constraints, i.e., must-link (positive) and cannot-link (negative) constraints. In this paper, besides the l 1 -norm regularization term, a normalized adapted affinity matrix is applied to embed the pairwise constraints in the affinity matrix. In the proposed affinity matrix, the weights are strengthened/weakened according to the positive/negative constraints. The experimental results on several data sets from University of California-Irvine (UCI) machine learning repository and a high dimensional data set, show the effectiveness of the proposed method in the classification tasks compared to some similar feature selection methods.

Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this paper we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.

Feature selection is one of the most important machine learning procedure, and it has been successfully applied to make a preprocessing before using classification and clustering methods. High-dimensional features often appear in big data, and it’s characters block data processing. So spectral feature selection algorithms have been increasing attention by researchers. However, most feature selection methods, they consider these tasks as two steps, learn similarity matrix from original feature space (may be include redundancy for all features), and then conduct data clustering. Due to these limitations, they do not get good performance on classification and clustering tasks in big data processing applications. To address this problem, we propose an Unsupervised Feature Selection method with graph learning framework, which can reduce the redundancy features influence and utilize a low-rank constraint on the weight matrix simultaneously. More importantly, we design a new objective function to handle this problem. We evaluate our approach by six benchmark datasets. And all empirical classification results show that our new approach outperforms state-of-the-art feature selection approaches.