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feature selection: Key to enhance node classification with graph neural networks

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 14-28页

作者： Sunil Kumar Maurya Xin Liu Tsuyoshi Murata Tokyo Institute of Technology TokyoJapan AIRC AISTTokyoJapan

Graphs help to define the relationships between entities in the data.These relationships,represented by edges,often provide additional context information which can be utilised to discover patterns in the data.Graph Neural Networks(GNNs)employ the inductive bias of the graph structure to learn and predict on various tasks.The primary operation of graph neural networks is the feature aggregation step performed over neighbours of the node based on the structure of the graph.In addition to its own features,for each hop,the node gets additional combined features from its neighbours.These aggregated features help define the similarity or dissimilarity of the nodes with respect to the labels and are useful for tasks like node classification.However,in real-world data,features of neighbours at different hops may not correlate with the node's features.Thus,any indiscriminate feature aggregation by GNN might cause the addition of noisy features leading to degradation in model's performance.In this work,we show that selective aggregation of node features from various hops leads to better performance than default aggregation on the node classification task.Furthermore,we propose a Dual-Net GNN architecture with a classifier model and a selector model.The classifier model trains over a subset of input node features to predict node labels while the selector model learns to provide optimal input subset to the classifier for the best performance.These two models are trained jointly to learn the best subset of features that give higher accuracy in node label predictions.With extensive experiments,we show that our proposed model outperforms both feature selection methods and state-of-the-art GNN models with remarkable improvements up to 27.8%.

关键词： classification feature selection neural network

feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

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Journal of Iron and Steel Research(International) 2022年第8期29卷 1205-1223页

作者： Zong-xin Chen Hui Liu Long Qi Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500YunnanChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500YunnanChina

Basic oxygen furnace(BOF)steelmaking end-point control using soft measurement models has essential value for economy and environment.However,the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of models.The traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial applications.In order to eliminate the negative impact of feature selection application in the BOF steelmaking,an improved grey wolf optimizer(IGWO)for feature selection was proposed,and it was applied to the BOF data set.Firstly,the proposed algorithm preset the size of the feature subset based on the new encoding scheme,rather than the traditional uncertain number strategy.Then,opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal solution.In addition,a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random methods.These methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking data.Finally,the proposed algorithm was compared with other feature selection algorithms on the BOF data sets.The results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content,which can improve the performance of the BOF steelmaking.

关键词： Basic oxygen furnace feature selection Grey wolf optimization Regression Opposition-based learning

feature selection with Deep Reinforcement Learning for Intrusion Detection System

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Computer Systems Science & Engineering 2023年第9期46卷 3339-3353页

作者： S.Priya K.Pradeep Mohan Kumar Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India

An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on feature selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

关键词： Intrusion detection security reinforcement learning machine learning feature selection beetle antenna search

feature selection with Optimal Stacked Sparse Autoencoder for Data Mining

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Computers, Materials & Continua 2022年第8期72卷 2581-2596页

作者： Manar Ahmed Hamza Siwar Ben Haj Hassine Ibrahim Abunadi Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia

Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based feature selectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

关键词： Data mining pattern recognition feature selection data classification SSAE model

feature selection for Cluster Analysis in Spectroscopy

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Computers, Materials & Continua 2022年第5期71卷 2435-2458页

作者： Simon Crase Benjamin Hall Suresh N.Thennadil College of Engineering IT&EnvironmentCharles Darwin UniversityCasuarinaNT 0909Australia Defence Science and Technology Group Edinburgh5111Australia Energy and Resources Institute Charles Darwin UniversityCasuarinaNT 0909Australia

Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy,namely,high dimensionality and small sample size.In order to improve cluster analysis outcomes,feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality.However,for cluster analysis,this must be done in an unsupervised manner without the benefit of data labels.This paper presents a novel feature selection approach for cluster analysis,utilizing clusterability metrics to remove features that least contribute to a dataset’s tendency to cluster.Two versions are presented and evaluated:The Hopkins clusterability filter which utilizes the Hopkins test for spatial randomness and the Dip clusterability filter which utilizes the Dip test for unimodality.These new techniques,along with a range of existing filter and wrapper feature selection techniques were evaluated on eleven real-world spectroscopy datasets using internal and external clustering indices.Our newly proposed Hopkins clusterability filter performed the best of the six filter techniques evaluated.However,it was observed that results varied greatly for different techniques depending on the specifics of the dataset and the number of features selected,with significant instability observed for most techniques at low numbers of features.It was identified that the genetic algorithm wrapper technique avoided this instability,performed consistently across all datasets and resulted in better results on average than utilizing the all the features in the spectra.

关键词： Cluster analysis spectroscopy unsupervised learning feature selection wavenumber selection

feature selection for Multi-label Classification Using Neighborhood Preservation

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 320-330页

作者： Zhiling Cai William Zhu Laboratory of Granular Computing and AI Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China

Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.

关键词： feature selection multi-label learning neighborhood relationship preserving sample similarity

feature selection for energy system modeling: Identification of relevant time series information

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能源与人工智能(英文)

能源与人工智能(英文) 2021年第2期4卷 16-29页

作者： Inga M.Muller Chair of Renewable and Sustainable Energy Systems Technical University of MunichAccisstrasse 21Munich 80333Germany

Heuristic or clustering based time series aggregation methods are often used to reduce temporal complexity of energy system models by selecting representative days.However,these methods potentially neglect relevant information of time series(e.g.,distribution parameters).To identify relevant time series parameters,feature selection algorithms can be applied.The present research contributes by(a)developing a new feature selection approach based on clustering,nested modeling and regression(CNR)which is designed for applications requiring high selectivity and using different data sets,(b)comparing and evaluating CNR with feature selection methods available from the literature(e.g.,LASSO)and(c)identifying relevant information of the time series applied in energy system models,in particular those of demand,photovoltaic and wind.Results show that CNR achieves on average up to 101%lower mean absolute errors when methods are directly compared.Thus,CNR better identifies relevant information when the number of selected features is restricted.The disadvantage of CNR,however,is its high computational effort.A potential remedy to counter this is the combination with another method(e.g.,as pre-feature selection).In terms of relevant information,energy systems including photovoltaic are mainly characterized by the correlation between demand and photovoltaic time series as well as the range and the 35%quantile of demand.When energy systems include wind power,the minimum and mean of wind as well as the correlation between demand and wind time series are relevant characteristics.The implications of these findings are discussed.

关键词： Energy system modeling feature selection Time series analysis Nested modeling Clustering Regression Intermittent renewable energies

feature selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

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Computers, Materials & Continua 2018年第6期55卷 419-433页

作者： Shengqun Fang Zhiping Cai Wencheng Sun Anfeng Liu Fang Liu Zhiyao Liang Guoyan Wang College of Computer National University of Defense TechnologyChangsha 410073China Computer Science and Technology Central South UniversityChangsha 999078China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Faculty of Information Technology Macao University of Science and TechnologyMacao 410073MO. Xuzhou University of Technology Xuzhou 221002China

By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical care.However,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in diagnosis.Therefore,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain.In this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the performance.To select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test.We evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and accuracy.Extensive experiments have conducted on a real-world Chinese electronic medical record database.The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.

关键词： Medical expert system EMR multi-label classification feature selection class discriminative degree

A Novel Method Based on Nonlinear Binary Grasshopper Whale Optimization Algorithm for feature selection

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Journal of Bionic Engineering 2023年第1期20卷 237-252页

作者： Lingling Fang Xiyue Liang Department of Computing and Information Technology Liaoning Normal UniversityDalian116081China

feature selection(FS)is considered as an important preprocessing step in data mining and is used to remove redundant or unrelated features from high-dimensional data.Most optimization algorithms for FS problems are not balanced in search.A hybrid algorithm called nonlinear binary grasshopper whale optimization algorithm(NL-BGWOA)is proposed to solve the problem in this paper.In the proposed method,a new position updating strategy combining the position changes of whales and grasshoppers population is expressed,which optimizes the diversity of searching in the target domain.Ten distinct high-dimensional UCI datasets,the multi-modal Parkinson's speech datasets,and the COVID-19 symptom dataset are used to validate the proposed method.It has been demonstrated that the proposed NL-BGWOA performs well across most of high-dimensional datasets,which shows a high accuracy rate of up to 0.9895.Furthermore,the experimental results on the medical datasets also demonstrate the advantages of the proposed method in actual FS problem,including accuracy,size of feature subsets,and fitness with best values of 0.913,5.7,and 0.0873,respectively.The results reveal that the proposed NL-BGWOA has comprehensive superiority in solving the FS problem of high-dimensional data.

关键词： feature selection Hybrid bionic optimization algorithm Biomimetic position updating strategy Nature-inspired algorithm-High-dimensional UCI datasets-Multi-modal medical datasets