The importance of medical data and the crucial nature of the decisions that are based on such data, as well as the large increase in its volume, has encouraged researchers to develop feature selection (FS)-based approaches to identify the most relevant data for specific medical problems In this paper, two intelligent wrapper FS approaches based on a new metaheuristic algorithm named the coronavirus herd immunity optimizer (CHIO) were applied with and without the incorporation of a greedy crossover (GC) operator strategy to enhance exploration of the search space by CHIO. The two proposed approaches, CHIO and CHIO-GC, were evaluated using 23 medical benchmark datasets and a real-world COVID-19 dataset. The experimental results indicated that CHIO-GC outperformed CHIO in terms of search capability, as reflected in classification accuracy, selection size, F-measure, standard deviation and convergence speed. The GC operator was able to enhance the balance between exploration and exploitation of the CHIO in the search and correct suboptimal solutions for faster convergence. The proposed CHIO-GC was also compared with two previous wrapper FS approaches, namely, binary moth flame optimization with Levy flight (LBMFO_V3) and the hyper learning binary dragonfly algorithm (HLBDA), as well as four filter methods namely, Chi-square, Relief, correlation-based feature selection and information gain. CHIO-GC surpassed LBMFO_V3 and the four filter methods with an accuracy rate of 0.79 on 23 medical benchmark datasets. CHIO-GC also surpassed HLBDA with an accuracy rate of 0.93 when applied to the COVID-19 dataset. These encouraging results were obtained by striking a sufficient balance between the two search phases of CHIO-GC during the hunt for correct solutions, which also increased the convergence rate. This was accomplished by integrating a greedy crossover technique into the CHIO algorithm to remedy the inferior solutions found during premature convergence and while locked into a local optimum search space.

Coronavirus herd immunity optimizer with greedy crossover for feature selection in medical diagnosis

Owing to the space–air–ground integrated networks (SAGIN), seaborne shipping has attracted increasing interest in the research on the motion behavior knowledge extraction and navigation pattern mining problems in the era of maritime big data for improving maritime traffic safety management. This study aims to develop a novel unsupervised methodology for feature extraction and knowledge discovery based on automatic identification system (AIS) data, allowing for seamless knowledge transfer to support trajectory data mining. The unsupervised hierarchical methodology is constructed from three parts: trajectory compression, trajectory similarity measure, and trajectory clustering. In the first part, an adaptive Douglas–Peucker with speed (ADPS) algorithm is created to preserve critical features, obtain useful information, and simplify trajectory information. Then, dynamic time warping (DTW) is utilized to measure the similarity between trajectories as the critical indicator in trajectory clustering. Finally, the improved spectral clustering with mapping (ISCM) is presented to extract vessel traffic behavior characteristics and mine movement patterns for enhancing marine safety and situational awareness. Comprehensive experiments are conducted and implemented in the Chengshan Jiao Promontory in China to verify the feasibility and effectiveness of the novel methodology. Experimental results show that the proposed methodology can effectively compress the trajectories, determine the number of clusters in advance, guarantee the clustering accuracy, and extract useful navigation knowledge while significantly reducing the computational cost. The clustering results are further explored and follow the Gaussian mixture distribution, which can help provide new discriminant criteria for trajectory clustering. 

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

As the proportion of photovoltaic (PV) power generation rapidly increases, accurate PV output power prediction becomes more crucial to energy efficiency and renewable energy production. There are numerous approaches for PV output power prediction. Many researchers have previously summarized PV output power prediction from different angles. However, there are relatively few studies that use machine learning methods as a means to conduct a separate review of PV output power prediction. This review classifies machine learning methods from different perspectives and provides a systematic and critical review of machine learning methods for recent PV output power applications in terms of the temporal and spatial scales of prediction and finds that the artificial neural network and support vector machine are used much more frequently than other methods. In addition, this study examines the differences between the output power prediction of individual PV plants and regional PV stations and the benefits of regional PV plant prediction, while this paper presents some performance evaluation matrices commonly used for PV output power prediction. In addition, to further improve the accuracy of machine learning methods for PV output power prediction, some researchers suggest preprocessing the input data of the prediction models or considering hybrid machine learning methods. Furthermore, the potential advantages of machine model optimization for prediction performance improvement are discussed and explored in detail.

Application of machine learning methods in photovoltaic output power prediction: A review

An interconnected system (ICS) is a complex industry system with multiple sensors, multiple tasks, and massive interaction. It is also of great importance for conducting the fault diagnosis technology research. Multi-fault diagnosis (MFD) is an urgent problem in engineering, while the complex mapping relationships among the system sensors, data patterns in single sensors, and fault modes in ICSs bringing severe challenges. The faults of ICS are similar to human disease in multiple dimensions. Enlightening the understanding of diseases in medicine guides us: hierarchical cognition and knowledge-data-fusion are important systematic ideas. Inspired by these, we propose a hierarchical cognize framework (HCF), which covers the cognition of sensors, data patterns in single sensors, and data climates. Subsequently, we propose a fuzzy neighbourhood three-way decision (FN3WD), experience fused self-adaptation Gaussian-mixture-model (EFSA-GMM), and coding-with-knowledge-discrimination (CWKD) to construct an HCF. To comprehensively verify the HCF, we successfully apply the HCF to the MFD of a satellite power system. Classic models of two-mainstream strategies are introduced as comparisons, specifically, MC-DCNN, MC-SVM, ML-DCNN, and ML-SVM. Compared to the comparative models, the HCF performs an increase of 12.35%, 7.72%, 6.90%, and 8.10% at least in accuracy, precision, recall, and F1-score, respectively, in 10 times cross-validation. Benefitting from the fusion of knowledge, the HCF has cognitive advantages in obtaining a high accuracy and precision diagnosis results. Meanwhile, the time consumption of the HCF is approximately 130 s, which is considerably reduced by as much as 50% compared with the deep learning models. 

Hierarchical cognize framework for the multi-fault diagnosis of the interconnected system based on domain knowledge and data fusion

This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are employed to generate the corresponding knockoffs of the selected features. The generated knockoffs are then fed into a classification module, in which two different classification models are used for the sake of fault diagnosis. Multiple experiments have been designed to investigate the effect of noise, fault resistance value, and sampling rate on the performance of the proposed framework. The effectiveness of the proposed framework is validated through a comprehensive study on the IEEE 118-bus system.

https://www.mdpi.com/1424-8220/21/15/5173/pdf

Xiaonan Liu

Papers

Gaussian mixture model with feature selection: An embedded approach